Visualizing Conceptual Lattice of Textual Data Using ToscanaJ

Abstract

Clustering is a technique which divides the data into objects of similar type. The term cluster analysis encompasses a number of different algorithms and methods for grouping objects of similar kind into respective categories. Cluster analysis is an exploratory data analysis tool which aims at sorting different objects into groups in a way that the degree of association between two objects is maximal if they belong to the same group and minimal otherwise. Clustering can be done on the basis of data or concepts. Concepts are necessary for representing human knowledge therefore it is beneficial to use Conceptual Clustering. Conceptual clustering is based on the concept that is it clusters the data on the basis of concept and formal context. It is distinguished from ordinary data clustering by generating a concept description. A system that store, process and present information using concept-oriented representation is called Conceptual Information system. Formal concept analysis uses the CIS for conceptual clustering. Formal Concept Analysis (FCA) is a technique that explains how the document clusters are clustered conceptually. FCA was introduced for modeling the concept in terms of lattice theory. It is a method for data analysis, knowledge representation and information management, representing the data in form of concept lattice after clustering. ToscanJ, a Java based package for creating CIS and viewing the different concept lattice is the tool used for implementing FCA.