Download On Clustering Validation Techniques

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HALKIDI, BATISTAKIS AND VAZIRGIANNIS
• Business. In business, clustering may help marketers discover significant groups in their
customers’ database and characterize them based on purchasing patterns.
• Biology. In biology, it can be used to define taxonomies, categorize genes with similar
functionality and gain insights into structures inherent in populations.
• Spatial data analysis. Due to the huge amounts of spatial data that may be obtained from
satellite images, medical equipment, Geographical Information Systems (GIS), image
database exploration etc., it is expensive and difficult for the users to examine spatial data
in detail. Clustering may help to automate the process of analysing and understanding
spatial data. It is used to identify and extract interesting characteristics and patterns that
may exist in large spatial databases.
• Web mining. In this case, clustering is used to discover significant groups of documents
on the Web huge collection of semi-structured documents. This classification of Web
documents assists in information discovery.
In general terms, clustering may serve as a pre-processing step for other algorithms, such
as classification, which would then operate on the detected clusters.
1.2.
Clustering algorithms categories
A multitude of clustering methods are proposed in the literature. Clustering algorithms can
be classified according to:
• The type of data input to the algorithm.
• The clustering criterion defining the similarity between data points.
• The theory and fundamental concepts on which clustering analysis techniques are based
(e.g. fuzzy theory, statistics).
Thus according to the method adopted to define clusters, the algorithms can be broadly
classified into the following types (Jain et al., 1999):
• Partitional clustering attempts to directly decompose the data set into a set of disjoint
clusters. More specifically, they attempt to determine an integer number of partitions that
optimise a certain criterion function. The criterion function may emphasize the local or
global structure of the data and its optimization is an iterative procedure.
• Hierarchical clustering proceeds successively by either merging smaller clusters into
larger ones, or by splitting larger clusters. The result of the algorithm is a tree of clusters,
called dendrogram, which shows how the clusters are related. By cutting the dendrogram
at a desired level, a clustering of the data items into disjoint groups is obtained.
• Density-based clustering. The key idea of this type of clustering is to group neighbouring
objects of a data set into clusters based on density conditions.
• Grid-based clustering. This type of algorithms is mainly proposed for spatial data mining.
Their main characteristic is that they quantise the space into a finite number of cells and
then they do all operations on the quantised space.