Hierarchical Clustering Using Evolutionary Algorithms

Abstract

Clustering is an important technique used in discovering some inherent structure present in data. The purpose of cluster analysis is to partition a given data set into a number of groups such that data in a particular cluster are more similar to each other than objects in different clusters. Hierarchical clustering refers to the formation of a recursive clustering of the data points: a partition into many clusters, each of which is itself hierarchically clustered. Hierarchical structures solve many problems in a large area of interests. In this paper a new evolutionary algorithm for detecting the hierarchical structure of an input data set is proposed. Problem could be very useful in economy, market segmentation, management, biology taxonomy and other domains. A new linear representation of the cluster structure within the data set is proposed. An evolutionary algorithm evolves a population of clustering hierarchies. Proposed algorithm uses mutation and crossover as (search) variation operators. The final goal is to present a data clustering representation to find fast a hierarchical clustering structure.

Evolutionary Hierarchical Time Series Clustering

“…The number of classes could be less than Nrnodes but not less than 2. The number of classes not included the root node which is labeled by 0 and contains the entire classification data set D [3]. The first step when apply an evolutionary algorithm for solving a problem is to find a suitable representation for candidate solutions.…”

“…A terminal class is a class that has no descendent is a terminal class. Classes corresponding to non-terminal nodes are called non-terminal classes [3].…”

“…The sequence is translated into a binary classification tree. Each node of the tree represents a potential cluster (a class) ( [3], [4], [5]). Classes of the classification tree are labeled by parsing each level from left to right and top to bottom.…”

“…The mining of time series data has attracted great attention in the data mining community in recent years ( [1], [2], [3], [4], [6], [7], [8]). Clustering time series is an important topic, motivated by several research challenges including similarity search of bioinformatics sequences, as well as the challenge of developing methods to recognize dynamic change in time series According to Keogh et al [5] there are two main categories in time series clustering: whole clustering and subsequence clustering.…”