Ascertaining Chronological Change Patterns in the Presence of Multiple Taxonomies

Abstract

Data mining and knowledge discovery (KDD) is the technique of converting raw data into useful information. It is predictive technique for interesting data analysis. Change mining is technique of data mining that finds and reports changes in mined item set from one time to another time. Different data mining algorithms are evolved to show correlation among data mined. The data association changes from one time to time. The project highlights the HIGEN (HIGHLY GENERLISED PATTERN) algorithm that reports minimum level of abstraction of frequently generalized pattern. Association between items shown by algorithm for data coming from real time applications at multiple level of taxonomy. The experiment performed on artificial and factual datasets to show competence and effectiveness of proposed approach as well as usefulness of real time application context.
DOI: 10.17762/ijritcc2321-8169.150101