A Way to Understand Various Patterns of Data Mining Techniques for Selected Domains

TL;DR

This paper explores various data mining patterns, focusing on classification-rule learning in large datasets, highlighting challenges related to scalability and execution time in data mining algorithms.

Contribution

It introduces a framework for understanding different data mining patterns and addresses scalability issues in classification-rule learning for large datasets.

Findings

Analysis of data mining pattern diversity

Identification of scalability challenges in large-scale classification

Proposed approaches to improve algorithm efficiency

Abstract

This has much in common with traditional work in statistics and machine learning. However, there are important new issues which arise because of the sheer size of the data. One of the important problem in data mining is the Classification-rule learning which involves finding rules that partition given data into predefined classes. In the data mining domain where millions of records and a large number of attributes are involved, the execution time of existing algorithms can become prohibitive, particularly in interactive applications.