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Unit 5: Closed Loop Marketing

as “Bhoemian Mix,” “Fur and Station Wagons,” and “Money and Brains,” for the group that Notes
captured the dominant lifestyles. Knowledge of lifestyles can be used to estimate the potential
demand for products (such as sports utility vehicles) services (such as pleasure cruises).
In finance, cluster analysis can be used for creating balanced portfolios: Given data on a variety
of investment opportunities (e.g., stocks), one may find clusters based on financial performance
variables such as return (daily, weekly, or monthly), volatility, beta, and other characteristics,
such as industry and market capitalization. Selecting securities from different clusters can help
create a balanced portfolio. Another application of cluster analysis in finance is for industry
analysis. For a given industry, we are interested in finding groups of similar firms based on
measures such as growth rate, profitability, market size, product range, and presence in various
international markets. These groups can then be analyzed in order to understand industry
structure and to determine, for instance, who is a competitor.
Cluster analysis can be applied to huge amounts of data. For instance, Internet search engines
use clustering techniques to cluster queries that user submit. These can then be used for improving
search algorithms.
Typically, the basic data used for clusters are a table of measurements on several variables,
where each column represents a variable and a row represents a record. Our goal is to form
groups of records so that similar records are in the same group. The number of clusters may be
pre-specified or determined from the data.

Application of Clustering

In business, clustering may help marketers discover distinct groups in their customer bases and
characterize customer groups based on purchasing patterns. In biology, it can be used to derive
plant and animal taxonomies, categorize genes with similar functionality, and gain insight into
structures inherent in populations. Clustering may also help in the identification of areas of
similar land use in an earth observation database, and in the identification of groups of motor
insurance policy holders with a high average claim cost, as well as the identification of groups
of houses in a city according to house type, value, and geographical location. It may also help
classify documents on the WWW for information discovery. As a data mining function, cluster
analysis can be used as a stand-alone tool to gain insight into the distribution of data, to observe
the characteristics of each cluster, and to focus on a particular set of clusters for further analysis.
Alternatively, it may serve as a preprocessing step for other algorithms, such as classification
and characterization, operating on the detected clusters.

Data clustering is under vigorous development. Contributing areas of research include data
mining, statistics, machine learning, spatial database technology, biology, and marketing. Owing
to the huge amounts of data collected in databases, cluster analysis has recently become a highly
active topic in data mining research. As a branch of statistics, cluster analysis has been studied
extensively for many years, focusing mainly on distance-based cluster analysis. Cluster analysis
tools based on k-means, k-medoids, and several other methods have also been built into many
statistical analysis software packages or systems, such as S-Plus, SPSS, and SAS.
In machine learning, clustering is an example of unsupervised learning. Unlike classification,
clustering and unsupervised learning do not rely on predefined classes and class-labelled training
examples. For this reason, clustering is a form of learning by observation, rather than learning
by examples. In conceptual clustering, a group of objects forms a class only if it is describable by
a concept. This differs from conventional clustering which measures similarity based on geometric
distance. Conceptual clustering consists of two components:

1. It discovers the appropriate classes, and
2. It forms descriptions for each class, as in classification.

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