Proje Yönetimi Sertifikalı Eğitim

Eğitim Adı: Proje Yönetimi

Toplam saat: 60

Neden bu eğitimi almalıyım?

Günümüz dünyasında hemen hemen tüm ülkeler bilimsel ve teknolojik gelişmeleri yakalamak ve kalkınmalarını sağlamak için çaba göstermektedirler. Bunun nedeni; ekonomik kalkınmaları sağlamak, uygar uluslar topluluğunun saygın ve rekabet gücüne sahip bir üyesi haline gelebilmektir. Bunları hedefleyen ülkeler bazı önemli projeleri gerçekleştirmek suretiyle ekonomik, sosyal ve kültürel gelişmeleri hızlandırmışlardır. Başarılı projeler gerçekleştiremeyen ülkeler ise, gelişmiş ülkelere bağımlı olmak zorunda kalmışlardır.

Projeler, işlerin kolayca görülebilir bir biçimde yapılandırılmasına, sistemler içinde düşünme ve çalışma yolunun açılmasına olanak sağlamaktadır. Sistemler;

·         Bütünsellik,

·         Verimlilik

·         İletişim,

·         Etkileşim ve

·         Süreklilik

ilkelerini içermektedir. Bütün bunları düzenlenip, planlanması ve geliştirilmesi ise başarılı bir proje yönetimini gerektirir.

Proje yönetimi;

·         Hizmetlerin iyileştirilmesinde,

·         Ürün kalitesinin artırılmasında,

·         Ekonomiklik ve karlılığın sağlanmasında,

·         Proje taraftarlarının ilişkilerinin düzenlenmesinde

liderliği onaylanmış bir yöntem bilimidir.

Proje yönetimi; bir hedefe ulaşma yolunda bilgi, yetenek, araç ve tekniğin uyumlu ve verimli kullanılmasını kolaylaştıran bir yaklaşımı içermektedir.

Proje yönetimi;

·         Etkin, etkili ve profesyonel bir yönetimi,

·         Yenilik ve yaratıcılık gerektiren çalışmaları,

·         Katılımın yanı sıra tutum ve davranış değişikliği içermektedir.

Başarılı bir proje yönetimi için proje yöneticiliği bilgi ve becerilerine sahip olmak gerekir. Proje yönetiminde sorunları çözerken, bütçe hazırlarken, kaynakları planlarken ve bunları kontrol ederken yol gösterecek geçmişe ait örnekler ve bilgiler yoktur. Ayrıca, proje yönetimi; yenlikler, sürprizler ve rekabet dolu yeni bir ortamdır.  Günümüzdeki anlayışla iş hayatında başarı sağlamak mümkün değildir. İş yapma biçimini yeniden düşünmek ve ilerlemeyi öngörmek durumundayız.

Proje yönetimi eğitimi ile beraber karmaşık ve büyük projelerin oluşturulması ve işletilmesi, proje koşullarının gerektirdiği niteliklere sahip etkin, etkili ve profesyonel kişiler yetiştirilecek. Proje yönetimi ile ilgili gerekli bütün bilgileri edinebilecekler. Ayrıca; iş hayatındaki sorunların nasıl tanımlandığını, nasıl planlanıp programlandığını, nasıl tanımlandığını, nasıl kontrol edildiğini ve nasıl değerlendirildiğini öğrenecekler. Daha önemlisi en karmaşık projelerin bile nasıl kolaylıkla ve başarılı bir biçimde yönetildiği açıklanacaktır.

Eğer sizde başarılı bir proje yöneticisi ya da başarılı bir lider olmak istiyorsanız, proje yönetimi eğitimi almalısınız!

Kaynak:

Proje Yönetimi Analizi ve Danışmanlık-Burhan Albayrak

Eğitim İçeriği:

48 Saat - Proje Yönetimi Metodolojisi

Bölüm 1 : Proje Yönetimi, Tanımlar ve Metodoloji 

• Proje,program, portföy tanımları
• Projeler vs. Operasyonlar
• Proje yönetimi tanımı ve metodolojiler
• Projelerde başarısızlık nedenleri
• Proje başarı faktörleri
• Proje yönetiminin organizasyonel faydaları
• Proje yöneticisi rolü ve sorumluluk alanları
• Projelerde paydaşlar
• Proje kısıt üçgeni
• Proje yönetimi metodolojisi
• Proje yaşam döngüsü
• Proje yönetimi vs. Program yönetimi
• Proje portfolyo yönetimi (PPM)
• PPM modelinin riskleri

Bölüm 2 : Organizasyonel Stratejiler ve Proje Seçimi

• Stratejiler vs. Projeler
• Projelerin sınıflandırılması
• Proje seçim kriterleri (Finansal ve finansal olmayan kriterler)
• Proje seçim yöntemleri (Ağırlık katsayılı matriks, ağırlıksız matriks, ikili karşılaştırma)

Bölüm 3 : Organizasyon Yapıları ve Proje Kültürü 

• Proje yönetiminde organizasyon yapıları
• Fonksiyonel organizasyon yapısı
• Proje tabanlı organizasyon yapısı
• Matriks organizasyon yapısı (güçlü, zayıf, dengeli)
• Doğru organizasyon yapısının seçilmesi
• Organizasyonlarda proje kültürünün oluşturulması

Bölüm 4 : Proje Yönetimi Süreç Grupları ve Bilgi Alanları 

• Proje yaşam döngüsü
• PMI’a göre proje yönetiminin 5 süreç grubu
• Projenin başlatılması
• Projenin planlanması
• Projenin yürütülmesi
• Projenin izlenmesi ve kontrolü
• Projenin kapatılması
• PMI’a göre proje yönetiminin 10 bilgi alanı
• Süreç grupları ve bilgi alanları arasındaki ilişki

Bölüm 5 : Proje Entegrasyon Yönetimi 

• Proje beratı hazırlama
• Proje yönetim planları
• Projelerde değişiklik kontrolü ve yönetimi
• Projelerin kapatılması

Bölüm 6 : Proje Kapsam Yönetimi 

• Proje hedeflerinin belirlenmesi
• Proje gereksinimlerinin analizi
• Kapsam tanımlama dokümanı
• Kapsam genişlemesi ve yönetimi
• Gereksinim analiz dokümanının oluşturulması
• Proje planı oluşturma
• İş Ayrışım Yapısı (WBS)
• WBS yapısı ve oluşturma teknikleri

Bölüm 7 : Proje Zaman Yönetimi 

• Çizelgeleme Araçları
• Bar şemaları ve ağ diyagramları
• Milestone(kilometretaşı) kavramı
• Gantt şeması oluşturma teknikleri
• Ağ diyagramı oluşturma teknikleri
• Aktivite ilişkilendirme metotları
• Kritik yol hesaplama teknikleri
• Bolluk hesaplama yöntemleri
• PERT ve diğer tahminleme teknikleri

Bölüm 8 : Proje Maliyet Yönetimi 

• Proje kontrolü
• Proje gelişiminin izlenmesi
• Durum raporları
• Bir performans yönetim aracı olarak “Kazanılmış Değer Analizi” (EVM)
• EVM’nin özellikleri
• EVM ile proje zaman ve maliyet kontrolü
• EVM formülleri
• EVM tekniğinin projelerde uygulanması
• EVM grafiksel analizi
• EVM tekniğinin sağladığı faydalar

Bölüm 9 : Proje Kalite Yönetimi 

• Kalite tanımları
• Kalite yönetim planı
• Kalite planlama
• Kalite güvence
• Kalite kontrol
• Deney tasarımı (Design of experiments)
• Pareto analizi
• Kalite ve 6 Sigma felsefesi
• DMAIC (define, measure, analyze, improve, control) yöntemi
• Kalite kontrol şemaları ve 7 tekrar kuralı
• IT projelerinde test süreci
• Test tipleri
• Kalite maliyetleri
• Proje kalitesinin iyileştirilmesi

Bölüm 10 : Proje Kaynak Yönetimi 

• Proje takımının oluşturulması
• Takım oluşturma aşamaları
• Sorumluluk atama matriksi
• Yetkinlik matriksi
• Kadro yönetim planı
• Proje takım tipleri ve yapıları

Bölüm 11 : Proje Risk Yönetimi 

• Risk yönetimi süreci
• Risk yanıt stratejileri
• Olasılık ve etki matriksi
• Risk kategorileri ve türleri
• Risklerin tanımlanması
• Risklerin analiz edilmesi
• Risklerin önceliklendirilmesi
• Kalitatif risk analizi
• Kantitatif risk analizi
• Risklerin kontrol edilmesi
• Risk yönetim planı

Bölüm 12 : Proje Satınalma Yönetimi 

• Satınalma yönetimi süreçleri
• Satınalma planlaması
• Pazar koşullarının değerlendirilmesi
• “Satın al” ya da “Yap” kararı
• Kontrat tipleri
• Kontrat tiplerine göre risk seviyeleri
• Tedarikçi seçim kriterleri
• Satınalma detayları

Bölüm 13 : Proje İletişim Yönetimi 

• İletişim ve proje yöneticisi
• İletişim yönetimi süreci
• İletişim tipleri
• İletişim stratejileri
• İletişim planının oluşturulması
• Toplantı yönetimi
• Yıkıcı takım profilleri

Bölüm 14 : Proje Paydaş Yönetimi 

• Paydaş tanımı
• Paydaşların analizi ve kilit paydaş kavramı
• Paydaş beklentilerinin doğru yönetilmesi
• Paydaşlar arası iletişim

12 Saat - Microsoft Project ile Proje Yönetimi

• Proje çalışma takviminin oluşturulması
• MS Project ile proje içeriğinin oluşturulması
• Faaliyet bolluk süreleri ve kritik faaliyetlerin analizi
• Kaynakların atanması ve ortak kaynak kullanımı (kaynak paylaşımı)
• MS Project ile proje takibi ve raporlama
• Kazanılmış değer analizleri:

Görevler

▪ Proje Planını Tanımlamak

▪ Takvim Yönetimi

▪ Proje Özelliklerini Girmek

▪ Görev Sürelerini Oluşturmak

▪ Bir Kilometre Taşı Girmek

▪ Özet Görevler

▪ Görevleri İlişkilendirmek

▪ Planı Biçimlendirmek ve Yazdırmak

▪ Gannt Grafiği İle Çalışmak

▪ Görev Sınırlandırmaları

▪ Kritik Yolun Analizi

▪ Görevdeki Çalışmayı Belirlemek

▪ Bağımsız Görev Takvimi

▪ Yinelenen Görev Tanımlamak

Proje Verilerini Düzenlemek

▪ Proje Verilerini Sıralamak

▪ Proje Verilerini Gruplandırmak

▪ Proje Verilerini Filtrelemek

Kaynak Yönetimi

▪ İnsan Kaynakları Havuzunu Oluşturmak

▪ Ekipman Kaynakları Havuzunu Oluşturmak

▪ Malzeme Kaynakları Havuzunu Oluşturmak

▪ Maliyet Kaynakları Listesini Oluşturmak

▪ Kaynak Maliyetleri İle Çalışmak

▪ Kaynak Çalışma Zamanını Ayarlamak

▪ Görevlere Kaynak Atamak

▪ Görevlere Ek Kaynaklar Atamak

▪ Göreve Maliyet Kaynağı Ataması ve Analizi

▪ Kaynak Yüklemelerini İncelemek ve Çözümlemek

▪ Fazla Yüklenmiş Kaynakları Seviyelendirmek

Proje Baseline

▪ Temel Planı

▪ İlerleyişi Güncellemek

▪ Yüzdesel Fiili Gerçekleştirme Verileri İle Çalışmak

▪ Birden Fazla Baseline Tanımlamak

▪ Proje Sapmalarını Analizi

Raporlar

▪ Raporları Tanımlamak

▪ Raporların İncelenmemesi ve Analizi

▪ Raporları Özelleştirmek

▪ Özet Raporlar Oluşturmak

▪ Raporları Yazdırmak

▪ Project ile Diğer Office Uygulamaları Arasında Veri Aktarımı

Proje Yönetimi

Günümüz dünyasında hemen hemen tüm ülkeler bilimsel ve teknolojik gelişmeleri yakalamak ve kalkınmalarını sağlamak için çaba göstermektedirler. Bunun nedeni; ekonomik kalkınmaları sağlamak, uygar uluslar topluluğunun saygın ve rekabet gücüne sahip bir üyesi haline gelebilmektir. Bunları hedefleyen ülkeler bazı önemli projeleri gerçekleştirmek suretiyle ekonomik, sosyal ve kültürel gelişmeleri hızlandırmışlardır. Başarılı projeler gerçekleştiremeyen ülkeler ise, gelişmiş ülkelere bağımlı olmak zorunda kalmışlardır.

Projeler, işlerin kolayca görülebilir bir biçimde yapılandırılmasına, sistemler içinde düşünme ve çalışma yolunun açılmasına olanak sağlamaktadır. Sistemler;

• ·         Bütünsellik,
• ·         Verimlilik
• ·         İletişim,
• ·         Etkileşim ve
• ·         Süreklilik

ilkelerini içermektedir. Bütün bunları düzenlenip, planlanması ve geliştirilmesi ise başarılı bir proje yönetimini gerektirir.

Proje yönetimi;

• ·         Hizmetlerin iyileştirilmesinde,
• ·         Ürün kalitesinin artırılmasında,
• ·         Ekonomiklik ve karlılığın sağlanmasında,
• ·         Proje taraftarlarının ilişkilerinin düzenlenmesinde

liderliği onaylanmış bir yöntem bilimidir.

Proje yönetimi; bir hedefe ulaşma yolunda bilgi, yetenek, araç ve tekniğin uyumlu ve verimli kullanılmasını kolaylaştıran bir yaklaşımı içermektedir.

Proje yönetimi;

• ·         Etkin, etkili ve profesyonel bir yönetimi,
• ·         Yenilik ve yaratıcılık gerektiren çalışmaları,
• ·         Katılımın yanı sıra tutum ve davranış değişikliği içermektedir.

Başarılı bir proje yönetimi için proje yöneticiliği bilgi ve becerilerine sahip olmak gerekir. Proje yönetiminde sorunları çözerken, bütçe hazırlarken, kaynakları planlarken ve bunları kontrol ederken yol gösterecek geçmişe ait örnekler ve bilgiler yoktur. Ayrıca, proje yönetimi; yenlikler, sürprizler ve rekabet dolu yeni bir ortamdır.  Günümüzdeki anlayışla iş hayatında başarı sağlamak mümkün değildir. İş yapma biçimini yeniden düşünmek ve ilerlemeyi öngörmek durumundayız.

Proje yönetimi eğitimi ile beraber karmaşık ve büyük projelerin oluşturulması ve işletilmesi, proje koşullarının gerektirdiği niteliklere sahip etkin, etkili ve profesyonel kişiler yetiştirilecek. Proje yönetimi ile ilgili gerekli bütün bilgileri edinebilecekler. Ayrıca; iş hayatındaki sorunların nasıl tanımlandığını, nasıl planlanıp programlandığını, nasıl tanımlandığını, nasıl kontrol edildiğini ve nasıl değerlendirildiğini öğrenecekler. Daha önemlisi en karmaşık projelerin bile nasıl kolaylıkla ve başarılı bir biçimde yönetildiği açıklanacaktır.

Eğer sizde başarılı bir proje yöneticisi ya da başarılı bir lider olmak istiyorsanız, proje yönetimi eğitimi almalısınız!

Kaynak:

Proje Yönetimi Analizi ve Danışmanlık-Burhan Albayrak

Article: Data Mining Case Study

Introduction

Constantly evolving technology has made it easier to keep the data. In situation some methods have been developed for the analysis of data. The science that collects these methods together is called data mining. In this article we will examine Data mining and its algorithms. Our goal is to talk about data mining and algorithms in generally. Then we will examine the clustering algorithm from the algorithms, In which areas it is applied and for what purpose it is used. After these phase, we will focus on k-means from clustering algorithms and methodology of that. In the next step, we will have research question, we analyze data properties after we will solve in the WEKA application.

What is Data Mining?

Technology rapidly has increased, so the ease of accessing information. It is easy to store information as easily as it is easy to access the information,So the data is rapidy growing and there are a lot data in everywhere. This condition has to be processed and analyzed. Many data analysis algorithms are called data mining, which is gathered under one roof. So data mining is a process of extracting useful information from a heap. In conclusion, data mining is the process of useful information from extracting large data by solving a number of algorithms.

Data mining is a combination of many disciplines. These disciplines: database systems, statistics, machine learning, and pattern recognition. The algebraic, geometric, and probabilistic viewpoints of data play a key role in data mining. Given a dataset of n points in a d-dimensional space, the fundamental analysis and mining tasks covered in this book include exploratory data analysis, frequent pattern discovery, data clustering, and classification models, which are described next.(DATA MINING AND ANALYSIS Fundamental Concepts and Algorithms, MOHAMMED J. ZAKI and WAGNER MEIRA JR,Cambridge Universiy, Pg: 26).

Data mining mostly used  in  Market Analysis and Management , Corporate Analysis & Risk Management , Fraud Detection.  The main problems are finding target customer type, forecasting for future, and prevent. Also data mining can be used in many ways in everyday life. Some of these can be listed as follows:

·         Evaluation of treatment claims made to hospitals according to time, place and need will be helpful in the initial stage of epidemic risk assessment, control and resource planning.

·         A model that identifies the profiles of users of fugitive energy will allow effective fighting with fugitives at low cost, which will allow them to predict potential fugitive energy users.

·         A study aimed at predicting the intensity of highways by region and time will ensure that, for example, accident rates are minimized by correct resource planning at the right time.

·         When implementing public support schemes, the success of programs implemented through institutional risk scoring increases the amount of support to be given to organizations with the right amount and right goals. Reducing the amount of bad loans is the fact that the profits that are the risk of not paying when allocating credits have been identified.

Generally used data mining applications can be listed as follows: Marketing Banking Retailing and sales, Manufacturing and production, Brokerage and securities trading, Government and defense , Computer hardware and software, Airlines, Health care ,Broadcasting,  Homeland security, Insurance Police (Week4Presantation, Keziban Seçkin, AYBU, pg: 12)

If we look at the algorithms of data mining in generally, algoritms of classification,most common used  naive bayes and decision tree, algoritms of clustring algoritmları, most common used Hierarchy Clustering and K-means, Association Rules (Apriori Algorithm), Text Mining, Web Mining.

Classification used to analyze the historical data stored in a database and to automatically generate a model that can predict future behavior. Its objective is to find a derived model that describes and distinguishes data classes or concepts. (Week4Presantation, Keziban Seçkin, AYBU, pg: 8)

·         Naive Bayes based on classification so we need training data,classification algorithm building classifier or model and test data to estimate the accuracy of classification rules (supervised learning). A learned model can be used to make predictions.

·         Decision Tree A decision tree is a structure that includes a root node, branches, and leaf nodes. Each internal node denotes a test on an attribute, each branch denotes the outcome of a test, and each leaf node holds a class label. The topmost node in the tree is the root node.(Data Mining Tuturiol, tutoroils points, pg: 31)

Clustring Partitioning a database into segments in which the members of a segment share similar qualities. After we will examine this issue in detail.

Association A category of data mining algorithm that establishes relationships about items that occur together in a given record

Text mining Application of data mining to non-structured or less structured text files. It entails the generation of meaningful numerical indices from the unstructured text and then processing these indices using various data mining algorithms. That is, by scanning an existing text (looking at the number of words from which the word is spoken, looking at what the frequency range is, and repeating it), it yields a meaningful result.

Web mining The discovery and analysis of interesting and useful information from the Web, about the Web, and usually through Web-based tools (Week4Presantation, Keziban Seçkin, AYBU)

ALGORİTM  OF CLUSTRING

What is the Clustring

Classes emerging by keeping similar data close together are called clustering. Classification is used mostly as a supervised learning method, clustering for unsupervised learning. The logic of the cluster is like this: Increase intracellar similarity as much as possible, the difference between the clusters is as much as high. The clustering algorithm is the oldest algorithm.Clustring can use many areas for example we think that the records of the products that are received by a customer in a business. From these data sets, clustering algorithms can provide useful information. For example, how many produce which size and pieces the shirt  (small, medium, large eg.)

If we will give the definition of the cluster academically: Clustering is a standard procedure in multivariate data analysis. It is designed to explore an inherent natural structure of the data objects, where objects in the same cluster are as similar as possible and objects in different clusters are as dissimilar as possible

Clustering is an exploratory data analysis. Therefore, the explorer might have no or little information about the parameters of the resulting cluster analysis. In typical uses of clustering the goal is to determine all of the following: The number of clusters, The absolute and relative positions of the clusters, The size of the clusters, The shape of the clusters, The density of the clusters.

The cluster properties are explored in the process of the cluster analysis, which can be split into the following steps.

1. Definition of objects: Which are the objects for the cluster analysis?

2. Definition of clustering purpose: What is the interest in clustering the objects?

3. Definition of features: Which are the features that describe the objects?

4. Definition of similarity measure: How can the objects be compared?

5. Definition of clustering algorithm: Which algorithm is suitable for clustering the data?

6. Definition of cluster quality: How good is the clustering result? What is the interpretation? ( Clustering Algorithms and Evaluations, pg: 180).

Application of Cluster Analysis

Clustering analysis is mostly used in many area  such as market research, pattern recognition, data analysis, and image processing.

Clustering can also help marketers find different target groups in their customer base. And using these application they can describe their customer groups based on the purchasing sample.

In the field of biology, it can be used to derive plant and animal taxonomies, categorize genes with similar functions and gain insight into structures inherent to populations.

Clustering also helps in description of areas of similar land use in an earth observation database. It also helps in the description of groups of houses in a city according to house type, value, and geographic location.

Clustering also helps in classifying texts on the web for information exploration.

Clustering is also used in outlier detection applications such as detection of credit card fraud. (Fraud Detection)

Requirements of Clustering in Data Mining

The following statements throw light on why clustering is required in data mining: 

Scalability - We need extremely scalable clustering algorithms to agreement with wide databases.

Ability to deal with different kinds of attributes - Algorithms should be able to be operative on any kind of data such as interval-based (numerical) data, categorical, and binary data.

Discovery of clusters with attribute shape - The clustering algorithm should be capable of detecting clusters of arbitrary shape. They should not be bounded to only distance measures that tend to find spherical cluster of small sizes.

High dimensionality - The clustering algorithm should not only be able to handle low-dimensional data but also the high dimensional space.

 Ability to deal with noisy data - Databases contain noisy, missing or erroneous data. Some algorithms are sensitive to such data and may lead to poor quality clusters.

Interpretability - The clustering results should be interpretable, comprehensible, and usable.

Algoritm of Cluster

In this section we describe the most well-known clustering algorithms. The main reason for having many clustering methods is the fact that the notion of “cluster” is not precisely defined (Estivill-Castro, 2000). Consequently many clustering methods have been developed, each of which uses a different induction principle. Farley and Raftery (1998) suggest dividing the clustering methods into two main groups: hierarchical and partitioning methods. Han and Kamber (2001) suggest categorizing the methods into additional three main categories: density-based methods, model-based clustering and gridbased methods. An alternative categorization based on the induction principle of the various clustering methods is presented in (Estivill-Castro, 2000).

We talk  a little bit about Hierarchical Methods After that we focus K-means algoritm.

Hierarchical Methods

Hierarchical clustering involves creating clusters that have a predetermined ordering from top to bottom.

Applications such as;

•      the discovery of different customer groups in the grocery and the emergence of shopping patterns of these groups,

•      the classification of similar genes according to plant and animal classifications and functions in biology,

•      the classification of houses according to types, values and geographical places in city planning are typical clustering applications.

•      At the same time as clustering is used to classify documents for information discovery on the Internet. 

Summary of Hierarchal Clustering Methods

• No need to specify the number of clusters in advance.

 • Hierarchical structure maps nicely onto human intuition for some domains

• They do not scale well: time complexity of at least O(n2), where n is the number of total objects.

• Like any heuristic search algorithms, local optima are a problem.

• Interpretation of results is (very) subjective.

K-MEANS

One of the earliest clustering algorithms, was developed by J. B. Mac Queen in 1967,K means is an unsupervised clustering method. Groups data into K clusters and attempts to group data points to minimize the sum of squares distance to their central mean.There are two most important goals:

1- The values within the cluster are very similar.
2- Values outside the set are not as similar as possible

The main idea is to define a the center for each cluster. The number of clusters is determined randomly. The most diffucult phase is the select of k. Because if we choose little k numbers, the objects we want to arrive in different clusters, can fall to the same cluster or if we increase the number of clusters, we disperse the objects too much. After the number K is randomly determined by the person, the cluster centers are selected, randomly. The easiest way to select a center is to choose highly distant data. After center is selected ,clustered according to distance of data using Euclidean connections. Afterwards, new centers are assigned by iteration. These steps are repeated until each data belongs to a cluster. Because the K-Means assignment mechanism allows each dataset to belong to only one cluster.

In conclusion, data set separate k number clusters, after distance of each point  measure to centroid. So calculating mean. The name of algoritm is K-Means.

How K-Means Works

1) Randomly select ‘k’ cluster centers.
2) Calculate the distance between each data point and cluster centers.
3) Assign the data point to the cluster center whose distance from the cluster center is minimum of all the cluster centers..
4) Recalculate the new cluster center
5) Recalculate the distance between each data point and new obtained cluster centers.
6) If no data point was reassigned then stop, otherwise repeat.

ADVANTAGE:
*It is suitable to run large data sets and maproduce.
*Assuming clusters are symmetric
*Fast, sturdy and easy to understand.

DISADVANTAGE
*Difficult to assign the center of cluster (k).
*İf there are two similar dataset, kmeans can’t *understand there are two cluster.
*Different results can be obtained with different displays.
*Random selection of cluster centers is inefficient.
*Algoritm cant run for non-linear dataset.
*It is sensitive to noisy data. This data is included in the sets.

CONCLUSION OF THE ARTICLE

We examined data mining, and we learnt it is a machine learning. We must have analyzed data so we have to have some methodoliges. Data mining gather all these algoritm. As using these algoritms, to be easy to life. Because we understand, what is meaning a lot data and how can move for the future. Also preparing to strategies is so important. Using todays datas, firms will predict the future and applied some stratejic plans. In Conclusion data mining is most important for Information Era. Because, data can be reached everwhere, but understanding is a science.

Reference

Data Clustering: A Review A.K. JAIN Michigan State University M.N. MURTY Indian Institute of Science AND P.J. FLYNN The Ohio State University

Comparision Between data  Clustring algoritms, Osama Abu Abbas, Computer Science departmanr, Yarmouk University, Jordan.

From Data Mining to Knowledge Discovery in Databases , Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth  

Clustering Algorithms and Evaluations

An Efficient K-Means Clustering Algorithm, Khaled Alsabti Syracuse University, Sanjay Ranka University of Florida, Vineet Singh Hitachi America, Ltd.

Cluster analysis: Basic concepts and algoritms.

DATA CLUSTERING Algorithms and Applications,Edited by Charu C. Aggarwal Chandan K. Reddy

CLUSTERING METHODS, Lior Rokach Department of Industrial Engineering Tel-Aviv University, Oded Maimon Department of Industrial Engineering Tel-Aviv University

DATA MINING AND ANALYSIS, Fundamental Concepts and Algorithms MOHAMMED J. ZAKI Rensselaer Polytechnic Institute, Troy, New York WAGNER MEIRA JR. Universidade Federal de Minas Gerais, Brazil.

K-means Algorithm g Cluster Analysis in Data Mining Edited by Zijun Zhang

K-means algorithm ,Mark Herbster, University College London Department of Computer Science

K -means Clustering  Edited by Ke Chen

K-means Clustering via Principal Component Analysis,Chris Ding Xiaofeng He

An Efficient k-Means Clustering Algorithm: Analysis and Implementation Tapas Kanungo, Senior Member, IEEE, David M. Mount, Member, IEEE, Nathan S. Netanyahu, Member, IEEE, Christine D. Piatko, Ruth Silverman, and Angela Y. Wu, Senior Member, IEEE

Our lessons presentation

CUSTOMER RELATIONSHIP MANAGEMENT

CASE:

Never underestimate your Clients' Complaint, no matter how funny it might seem!

This is a real story that happened between the customer of General Motors and its Customer-Care Executive. Please read on.....

A complaint was received by the Pontiac Division of General Motors:

'This is the second time I have written to you, and I don't blame you for not answering me, because I sounded crazy, but it is a fact that we have a tradition in our family of Ice-Cream for dessert after dinner each night, but the kind of ice cream varies so, every night, after we've eaten, the whole family votes on which kind of ice cream we should have and I drive
down to the store to get it. It's also a fact that I recently purchased a new Pontiac and since then my trips to the store have created a problem.....

You see, every time I buy a vanilla ice-cream, when I start back from the store my car won't start. If I get any other kind of ice cream, the car starts just fine. I want you to know I'm serious about this question, no matter how silly it sounds "What is there about a Pontiac that makes it not start when I get vanilla ice cream, and easy to start whenever I get any other kind?" The Pontiac President was understandably skeptical about the letter, but sent an Engineer to check it out anyway.

The latter was surprised to be greeted by a successful, obviously well educated man in a fine neighborhood. He had arranged to meet the man just after dinner time, so the two hopped into the car and drove to the ice cream store. It was vanilla ice cream that night and, sure enough, after they came back to the car, it wouldn't start.

The Engineer returned for three more nights. The first night, they got chocolate. The car started. The second night, he got strawberry. The car started. The third night he ordered vanilla. The car failed to start.

Now the engineer, being a logical man, refused to believe that this man's car was allergic to vanilla ice cream. He arranged, therefore, to continue his visits for as long as it took to solve the problem. And toward this end he began to take notes: He jotted down all sorts of data: time of day, type of gas uses, time to drive back and forth etc.

In a short time, he had a clue: the man took less time to buy vanilla than any other flavor. Why? The answer was in the layout of the store. Vanilla, being the most popular flavor, was in a separate case at the front of the store for quick pickup. All the other flavors were kept in the back of the store at a different counter where it took considerably longer to check out
the flavor.

Now, the question for the Engineer was why the car wouldn't start when it took less time. Eureka - Time was now the problem - not the vanilla ice cream!!!! The engineer quickly came up with the answer: "vapor lock". 

It was happening every night; but the extra time taken to get the other flavors allowed the engine to cool down sufficiently to start. When the man got vanilla, the engine was still too hot for the vapor lock to dissipate.

Even crazy looking problems are sometimes real and all problems seem to be simple only when we find the solution with clear and logical thinking.

Don't just say it is " IMPOSSIBLE" without putting a sincere effort....
What really matters is your attitude and your perception.

´Customer relationship management (CRM) is an approach to managing a company's interaction with current and potential future customers that tries to analyze data about customers' history with a company and to improve business relationships with customers, specifically focusing on customer retention and ultimately driving sales growth

Types of CRM

´Operational:The primary goal of customer relationship management systems is to integrate and automate sales, marketing, and customer support. Therefore, these systems typically have a dashboard that gives an overall view of the three functions on a single page for each customer that a company may have. The dashboard may provide client information, past sales, previous marketing efforts, and more, summarizing all of the relationships between the customer and the firm. Operational CRM is made up of 3 main components: sales force automation, marketing automation, and service automation.

´Analytical

The role of analytical CRM systems is to analyze customer data collected through multiple sources, and present it so that business managers can make more informed decisions. Analytical CRM systems use techniques such as data mining, correlation, and pattern recognition to analyze the customer data. These analytics help improve customer service by finding small problems which can be solved, perhaps, by marketing to different parts of a consumer audience differently.For example, through the analysis of a customer base's buying behavior, a company might see that this customer base has not been buying a lot of products recently. After scanning through this data, the company might think to market to this subset of consumers differently, in order to best communicate how this company's products might benefit this group specifically.

´Collaborative

The third primary aim of CRM systems is to incorporate external stakeholders such as suppliers, vendors, and distributors, and share customer information across organizations. For example, feedback can be collected from technical support call, which could help provide direction for marketing products and services to that particular customer in the future.

A good CRM should provide support for the following functions:

• capture and maintain of customer needs, motivations, and behaviors over the lifetime of the relationship

• facilitate the use of customer experiences for continuous improvement of this relationship

• integrate marketing, sales, and customer support activities measuring and evaluating the process of knowledge acquisition and sharing

CRM systems in practice

´Call centers

´Contact center automation

´Social media

´Location-based services

´CRM systems for business-to-business transactions

CRM ANALYSIS PROCESSES

´LEAD MANAGEMENT The focus of this process is on organizing and prioritizing contacts with the prospective customers. It involves integration with campaign management and service management, as well as customer profiling. A sub process of lead management is customer scoring, which uses quantitative and qualitative measures to rank the customer based on his or her interest in the product or service. This filtering process allows for more precise target marketing and it lowers the contact costs.

´CUSTOMER PROFILING The focus of this process is to develop a marketing profile of every customer by observing his or her buying patterns, demographics, buying and communication preferences, and other information that allows categorization of the customer. The knowledge generated from this process feeds into campaign management, sales management, service management, and the other processes discussed earlier. In addition, this process allows more individualized contact with the customer.

´FEEDBACK MANAGEMENT A good CRM requires a closed-knowledge management loop that consolidates, analyzes, and shares the customer information collected by CRM delivery and support processes with the analysis process, and vice versa. The loop can provide a road map for continuous improvement process for the company's products and services. A good system will discard unnecessary data and focus mainly on the knowledge useful for making better decisions.

CRM COMPONENTS

´MARKET RESEARCH

´SALES FORCE AUTOMATION (S FA)

´CUSTOMER SERVICE AND SUPPORT

´DATA MINING AND ANALVTICS

´MARKET RESEARCH The two key functionalities here are campaign management and market analysis. Campaign management provides support for preparing such things as marketing budgets, ad placement, sales targeting, and response management. Marketing analysis tools provide statistical and demographic analysis, Web site traffic monitoring, and profiling tools. With the amount of data collected today these tools provide sophisticated segmenting and targeting capabilities in real time.

´SALES FORCE AUTOMATION (S FA) Sales force automation software has been around since long before CRM became a buzzword. Some of the current CRM vendors were originally in the SFA market. SFA tools provide basic functionality for sales personnel to automate sales lead distribution and tracking, sales reporting, pipeline management, contacts centralization and management, and group collaboration. In addition, they include such software for sales managers and executives as opportunity management, forecasting, reporting, analytics, and customizable dashboard capabilities so that they can be confident that their teams are producing at their full capacity. The goal of SFA software is to give businesses the upper hand with their sales data and to empower sales reps to spend more time selling and less time on administration.

´CUSTOMER SERVICE AND SUPPORT The customer service function has gone through major changes since the advent of the Internet. Online help desks have become a common source for customers to find quick answers to complex technical questions. Customer service originally consisted of setting up a call center with access to a customer database and the Frequently Asked Questions (FAQs) Web site page. Today, with sophisticated CRM back-ends, companies have been able to consolidate the two areas into help desk support centers. Customer service functionality typicaUy includes help desk ticket management software, e-mail, interactive chat, Web telephony, and other interaction tools connected to a fully integrated customer database, which is connected to the supply chain management and ERP application. These tools can be accessed by a trained help center agent or by customers directly via the Internet.

´DATA MINING AND ANALTICS The amount of data being generated by the Web-driven business has been a driver for data mining and analytics functionality because it represents an extension of existing product lines rather than the creation of new ones. Such businesses as Amazon and eBay generate gigabytes of data per day, and even small Web sites easily generate megabytes of data. This data must be collected, sorted, organized, and analyzed for trends, demographics, cross-selling opportunities, and identification of other sales patterns. Sophisticated OLAP and data mining software are often integrated with CRM packages.