No abstract

Time series clustering is an important topic, particularly for similarity search amongst long time series such as those arising in bioinformatics. In this paper a new evolutionary algorithm for detecting the hierarchical structure of an input time series data set is proposed. A new linear representation of the cluster structure within the data set is used. Proposed algorithm uses mutation and crossover as (search) variation operators. A new fitness function is proposed.

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.

A central problem in marketing is the clear understanding of consumer's choice or preferences. Designing questionnaires and then analyzing the answers of probable customers can achieve this. The traditional approach in the marketing analysis has been the designing of nonadaptive questionnaires, questionnaires that are predetermined and not at all influenced by respondent's answers. The aim of this paper is to design a questionnaire that is influenced by respondent's answer through implementation of soft computing and approximate reasoning methodologies. The learning of particular pattern on respondent's fuzzy responses has also been envisaged in the post-survey (Postconjoint) and further better clustering of choices and segregation is accomplished. The module of learning and finer clustering from respondent's choice pattern could be a major pre-requisite for construction of adaptive questionnaires. Further extensions of the soft computing methods for product recommender system have also been mentioned for the design of adaptive questionnaire.

No abstract