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Clustering and Segmentation

Segmentation is the process that groups similar objects together and forms clusters. Thus it is often referred to as clustering. Clustered groups are homogeneous within and desirably heterogeneous in between. The rationale of intra-group homogeneity is that objects with similar attributes are likely to respond somewhat similarly to a given action. This property has various uses both in business and in scientific research.

Self Organizing Maps (SOM) and Competitive Learning

Self Organizing Maps (SOM), also known as Kohonen Feature Maps, were developed to simulate the way that vision systems work in our brain. Organizations constructed with SOM are very useful in clustering data. It can automatically learn patterns present in data. SOM is based on Neural Network. It is noted that neural networks do not suffer greatly from the limitations discussed above. SOM uses competitive learning techniques to train networks (or to learn patterns). It is often referred to as "Winner takes all strategy", since nodes compete among themselves to display the strongest activation to a given data.

Neural Clustering

CMSR provides a neural clustering procedure that is based on SOM. Neural clustering can be best explained with the figures shown below. In the figures, objects are placed into two dimensional grid cells. There are 81 cells from 9 rows and 9 columns. Note that some cells are empty with no members. Each cell contains most similar objects, i.e., having many similar properties. Objects in neighboring (or nearby) cells are also similar in nature. Closeness of cell distance indicates high degree of similarity. Neural clustering exhibits the following advantages;

  • Does not greatly suffer from limitations as in other techniques.
  • No extensive data preparation effort is required.
  • Clusters are organized in a way that shows closeness of objects in other clusters.
  • Provide rich visualization as shown in "Segment Analysis".
  • Most importantly, robust pattern detection and clustering.
Segment Analysis

In the left figure, colored circles are pie charts representing distribution for combination of gender and race. Notice that all cells contain objects of the same single type. Furthermore, nearby cells of the same type objects are clustered together. An example of perfect clustering! The middle figure shows histograms for a numerical variable. You will notice that nearby cells have similar distributions. The right is all-in-one distribution charts for a specific cell segment.

Segmentation cluster analysis. Clustering segment analysis. Cell segment analysis.

Neural clustering is robust in detecting patterns and organizes them in a way that provides powerful cluster visualization, as shown in the above figures. This is extremely useful with marketing and business data. The following is another example of neural clustering. This example is based two numerical variables. You can easily find this type of clustering in scientific research. Notice that how well neural clustering works both numerical and categorical data.

Clustering example.

Statistical Predictive Segmentation Modeling

Generally, clustering tools do only one thing. That is to cluster similar objects together of a given dataset. CMSR neural clustering is a segmentation modeling system. It builds segmentation models. Then models are used to segment not only the dataset used for network training but also other datasets stored in database systems. In addition, it can be used for predictive modeling for statistical inferences: probabilities, averages, and classification. As a matter of fact, CMSR uses predictive segmentation modeling to perform data segmentation! Network is trained to learn clustering patterns as well as statistics of resulting segmentation. Then, the network is applied to datasets for segmentation and statistical value prediction. CMSR predictive segmentation modeling is based on Radial Basis Functions (RBF). It's a variant form that allows flexible use of models. The most prominent use of segmentation modeling is behavior modeling on time-variant variables. In addition, it can be used as an alternative modeling method to standard predictive modeling methods.

Behavioral modeling on Time-variant variables

The very idea of clustering along the similar attributes is that people (or objects) with similar properties tend to exhibit similar behaviors. It is very important to note that the similarity in terms of attributes may change over time. Clustering with time-varying variables can be a challenging task with other techniques. In RFM database marketing, for example, it is a common practice to segment customers based on "recency", "frequency", and "monetary values". Once marketing campaign is completed, the values change. Modeling on neither previous nor current values will produce optimal results. It is desirable to develop segmentation models based on the values at the time of previous campaigns, and to segment the customers based on the current values (or at the time of catalog mailing) using the segmentation models. The result will be better segmentation that will exhibit more predictive power! For more, click RFM Segmentation Marketing.

Predictive Modeling vs Statistical Predictive Modeling

The main difference between predictive modeling and statistical predictive segmentation modeling is the sorts of values they predict. In a nutshell, segmentation modeling predicts statistical aggregates such as probabilities and averages. Note that segmentation induces partitions that may consist of multiple instances on which statistical aggregates can be inferred. Predictive modeling, on the other hand, is not designed to predict aggregate values. Segmentation modeling is useful in situations where events interested have very low frequency of occurring and, thus, application of predictive modeling becomes un-suitable. Such events can be best inferred with attached probabilities or averages. Typical examples may include catalog mail marketing, insurance scoring and credit scoring, and so on.

Segmentation Variable Selection Methods

Although neural clustering can automatically adjust variable weights, it is often desirable to work only with variables of significant importance. Limiting to such variables can generate segments with simple and clean profiles. It is noted that careful segmentation-target variable selection is essential in predictive segmentation modeling. Unlike standard predictive modeling, predictive segmentation modeling relies on modeler's manual selection of predictive variables. Otherwise, segmentation may not induce models that show predictive power.

Identification of significant variables can be very difficult without proper tools. CMSR link analysis and predictive neural network can be used for analyzing variable's significance. It is noted that selection of segmentation variables using link analysis and/or predictive neural network assures that segmentation results will have predictive power. For more on segmentation variable selection methods, read Link Analysis.

For more about customer segmentation, please read Customer Segmentation.

Data Mining Tools for Segmentation

CMSR Data Miner supports segmentation tools based on neural networks. For software information and downloads, please read CMSR Data Miner.