Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Abstract

newlineKnowledge discovery in databases is the non trivial process of identifying valid novel potentially useful and ultimately understandable patterns from dataThe term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the dataTo infer patterns from data data mining involves different approaches like association rule mining, classification techniques or clustering techniques Among the many data mining techniques clustering plays a major role since it helps to group the related data for assessing properties and drawing conclusionsMost of the clustering algorithms act on a dataset with uniform format since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters If a dataset consists of mixed attributes a combination of numerical and categorical variables a preferred approach is to convert different formats into a uniform format The research study explores the various techniques to convert the mixed data sets to a numerical equivalentso as to make it equipped for applying the statistical and similar algorithmsThe results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set The thesis also proposes an extension to the well known algorithm for handling mixed data types to deal with data sets having only categorical data The proposed conversion has been validated on a data set corresponding to breast cancer Moreover, another issue with the clustering process is the visualization of output Different geometric techniques like scatter plot or projection plots are available but none of the techniques display the result projecting the whole database but rather demonstrate attribute pair wise analysis newline newline