Understanding Cluster Analysis
Cluster analysis is a convenient method for identifying homogenous groups of objects called clusters. Objects (or cases, observations) in a specific cluster share many characteristics, but are very dissimilar to objects not belonging to that cluster. Let’s try to gain a basic understanding of the cluster analysis procedure by looking at a simple example. Imagine that you are interested in segmenting your customer base in order to better target them through, for example, pricing strategies. The first step is to decide on the characteristics that you will use to segment your customers. In other words, you have to decide which clustering variables will be included in the analysis. For example, you may want to segment a market based on customers’ price consciousness (x) and brand loyalty (y). These two variables can be measured on a 7-point scale with higher values denoting a higher degree of price consciousness and brand loyalty. The values of seven respondents are shown in Table 9.1 and the scatter plot in Fig. 9.1.

The objective of cluster analysis is to identify groups of objects (in this case, customers) that are very similar with regard to their price consciousness and brand loyalty and assign them into clusters. After having decided on the clustering variables (brand loyalty and price consciousness), we need to decide on the clustering procedure to form our groups of objects. This step is crucial for the analysis, as different procedures require different decisions prior to analysis. There is an abundance of different approaches and little guidance on which one to use in practice. We are going to discuss the most popular approaches in market research, as they can be easily computed using SPSS. These approaches are: hierarchical methods, partitioning methods (more precisely, k-means), and two-step clustering, which is largely a combination of the first two methods. Each of these procedures follows a different approach to...

...Chapter
16
ClusterAnalysis
Identifying groups of individuals or objects that are similar to each other but different from individuals in other groups can be intellectually satisfying, profitable, or sometimes both. Using your customer base, you may be able to form clusters of customers who have similar buying habits or demographics. You can take advantage of these similarities to target offers to subgroups that are most likely to be receptive to them. Based on scores on psychological inventories, you can cluster patients into subgroups that have similar response patterns. This may help you in targeting appropriate treatment and studying typologies of diseases. By analyzing the mineral contents of excavated materials, you can study their origins and spread.
Tip: Although both clusteranalysis and discriminant analysis classify objects (or cases) into categories, discriminant analysis requires you to know group membership for the cases used to derive the classification rule. The goal of clusteranalysis is to identify the actual groups. For example, if you are interested in distinguishing between several disease groups using discriminant analysis, cases with known diagnoses must be available. Based on these cases, you derive a rule for classifying undiagnosed patients. In cluster...

...Summary on
‘Object-Based Image Analysis Using Multiscale Connectivity.’
This paper precedes a way for image analysis based on the concept of multiscale
connectivity. The authors have suggested an approach to design several tools for object-based
image representation and analysis, which attain the connectivity structure of images in a multiscale
fashion. More specifically, they have suggested a nonlinear pyramidal image representation
scheme, which decomposes an image at various scales by means of multiscale grain filters. These
filters progressively remove connected components from an image that fail to satisfy a given
benchmark. They have also used the concept of multiscale connectivity to design a hierarchical
data partitioning tool and apply this to construct another image representation scheme, based on
the theory of component trees, which organizes partitions of an image in a hierarchical multiscale
fashion. They have also suggested a geometrically-oriented hierarchical clustering algorithm
which generalizes the classical single-linkage algorithm. Finally suggested two object-based
multiscale image summaries, similar to the well-known pattern spectrum, which can be useful in
image analysis and image understanding applications.
Multiscale connectivity was introduced by extending a general theory of connectivity on
complete lattices, to a multiscale setting. The idea of multiscale connectivity...

...of Dr. Miller’s initial study on historical movie taglines. This follow-up analysis considered movie taglines between 1979 and 2014 which relates to my own personal “movie watching years”. The goal was to employ additional strategies including stemming and looking at various combinations of clustering algorithms, pairwise distance metrics and words extracted to create the terms by document matrix to understand impact on cluster efficiency. Ultimately looking to answer the question of how movies classes may have changed over the last 35 years based on movie taglines and does it seem consistent with my own observation over the past 35 years.
The final model resulted in 6 clusters named as follows: American_Music, Fear_Evil, Action_Minded, Anything_Fat, Forced_Away , Beyond_Criminal . A chart of standardized text measures is provided at the right. This chart allows for a couple of conclusions:
1. For the most part there has been consistency over the period of time with convergence towards the end
2. We saw a rise into the 2000s of what appear to be health related reality shows
3. The last few years show a spike up in criminal drama
In addressing the question how have the classes changed over the last 35 years there seems to be a lot of neutrality and not a clear conclusion as I had expected. Further work on the clustering and feature extraction may help to improve.
Results of Analysis
The analysis...

...Chapter 9
ClusterAnalysis
Learning Objectives
After reading this chapter you should understand: – The basic concepts of clusteranalysis. – How basic cluster algorithms work. – How to compute simple clustering results manually. – The different types of clustering procedures. – The SPSS clustering outputs.
Keywords Agglomerative and divisive clustering Á Chebychev distance Á City-block distance Á Clustering variables Á Dendrogram Á Distance matrix Á Euclidean distance Á Hierarchical and partitioning methods Á Icicle diagram Á k-means Á Matching coefﬁcients Á Proﬁling clusters Á Two-step clustering Are there any market segments where Web-enabled mobile telephony is taking off in different ways? To answer this question, Okazaki (2006) applies a twostep clusteranalysis by identifying segments of Internet adopters in Japan. The ﬁndings suggest that there are four clusters exhibiting distinct attitudes towards Web-enabled mobile telephony adoption. Interestingly, freelance, and highly educated professionals had the most negative perception of mobile Internet adoption, whereas clerical ofﬁce workers had the most positive perception. Furthermore, housewives and company executives also exhibited a positive attitude toward mobile Internet usage. Marketing managers can now use these results to better target speciﬁc customer segments via mobile...

...QUANTITATIVE TECHNIQUE
TOPIC:CLUSTER ANALYSIS USING SPSS
INTRODUCTION
Grouping similar customers and products is a fundamental marketing activity. It is
used, prominently, in market segmentation. As companies cannot connect with all
their customers, they have to divide markets into groups of consumers, customers,
or clients (called segments) with similar needs and wants. Firms can then target
each of these segments by positioning themselves in a unique segment (such as
Ferrari in the high-end sports car market). While market researchers often form market segments based on practical grounds, industry practice and wisdom, clusteranalysis allows segments to be formed that are based on data that are less dependent
on subjectivity.
The segmentation of customers is a standard application of clusteranalysis, but it
can also be used in different, sometimes rather exotic, contexts such as evaluating
typical supermarket shopping paths or deriving employers’
branding strategies.
MEANING:
Clusteranalysis or clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine...

...Non-Hierarchical ClusterAnalysis
Non-hierarchical clusteranalysis (often known as K-means Clustering Method) forms a grouping of a set of units, into a pre-determined number of groups, using an iterative algorithm that optimizes a chosen criterion. Starting from an initial classification, units are transferred from one group to another or swapped with units from other groups, until no further improvement can be made to the criterion value. There is no guarantee that the solution thus obtained will be globally optimal - by starting from a different initial classification it is sometimes possible to obtain a better classification. However, starting from a good initial classification much increases the chances of producing an optimal or near-optimal solution.
(source: http://www.asreml.com/products/genstat/mva/NonHierarchicalClusterAnalysis.htm)
The algorithm is called k-means, where k is the number of clusters you want; since a case is assigned to the cluster for which its distance to the cluster mean is the smallest. The action in the algorithm centers on finding the k-means. You start out with an initial set of means and classify cases based on their distances to the centers. Next, you compute the cluster means again, using the cases that are assigned to the cluster; then, you reclassify all cases based on the new set of means. You keep...

...CLUSTERANALYSIS:
ALGORITHMS AND ANALYSIS USING SAS
BY: AHMED ALDAHHAN
SUPERVISED BY: LECTURER JING XU
BIRKBECK UNIVERSITY OF LONDON
2013/2014
ABSTRACT
The scope of this paper is to provide an introduction to clusteranalysis; by giving a general background for
clusteranalysis; and explaining the concept of clusteranalysis and how the clustering algorithms work. A
basic idea and the use of each clustering method will be described with its graphical features. Different
clustering techniques are also explained with examples to get a better idea. The two main clustering
techniques (Hierarchical and K-means Partitioning) are illustrated using a sample data set ‘IRIS FLOWER DATA
SET’ (1936), where a comparison of the two methods is made based on data suitability and model
performance.
TABLE OF CONTENTS
CHAPTER 1
1.0
Introduction …………………………………………………………………………………………………….. 5
1.1
Understanding ClusterAnalysis ……………..……………………….……………………………….. 7
CHAPTER 2
2.0
Definitions …………………………………………………………………………..………………………..… 9
2.1
The Data Matrix ………………………………………………………..…….…………………………….… 9
2.2
The Proximity matrix ………………………………………………………………….……………………. 9
2.3
Similarity and Dissimilarity Matrices ………..………………..………………………………..…. 11
2.4
Different Types of Clusters...

...ITKM
Analysis of Data Mining
The article Data Mining by Christopher Clifton analyzed how different types of data mining techniques have been applied in crime detection and different outcomes. Moreover, the analysis proposed how the different data mining techniques can be used in detection of different form of frauds. The analysis gave the advantages and disadvantages of using data mining in different operation. The major advantage was that data mining enables analysis of large quantities of data. This is important since such data cannot be analyzed manually since the data is often complex for humans to understand. However, data mining techniques have been used for deceitful purposes such as inappropriate disclosure of private information. The article analyzed different data mining techniques. Predictive modeling is one such technique used in estimation of particular target attribute. Descriptive modeling was another technique, which entails dividing data into groups. The other techniques described include pattern mining used in identification of rules relating to different data pattern and anomaly detection, which entails determining the unusual instances that, may arise when using the different data-mining model.
1) What is the title and what was the objective of the study/analysis)
The title of the article was data mining. The article focused on skills in knowledge discovery...