A Novel Modified Apriori Approach for Web Document Clustering

TL;DR

This paper introduces a modified apriori algorithm that reduces database scans and enhances association rule analysis for web document clustering, further improving cluster quality with Fuzzy C-Means, K-Means, and VSM techniques, demonstrated on large datasets.

Contribution

A new modified apriori approach that improves efficiency and association analysis in web document clustering, outperforming traditional apriori on large datasets.

Findings

Modified apriori reduces database scans and improves association analysis.

Fuzzy C-Means outperforms K-Means and VSM in cluster F-measure.

Approach scales well to large datasets with over 10,000 documents.

Abstract

The traditional apriori algorithm can be used for clustering the web documents based on the association technique of data mining. But this algorithm has several limitations due to repeated database scans and its weak association rule analysis. In modern world of large databases, efficiency of traditional apriori algorithm would reduce manifolds. In this paper, we proposed a new modified apriori approach by cutting down the repeated database scans and improving association analysis of traditional apriori algorithm to cluster the web documents. Further we improve those clusters by applying Fuzzy C-Means (FCM), K-Means and Vector Space Model (VSM) techniques separately. For experimental purpose, we use Classic3 and Classic4 datasets of Cornell University having more than 10,000 documents and run both traditional apriori and our modified apriori approach on it. Experimental results show that our approach outperforms the traditional apriori algorithm in terms of database scan and improvement on association of analysis. We found out that FCM is better than K-Means and VSM in terms of F-measure of clusters of different sizes.