Hierarchical Approach for Online Mining--Emphasis towards Software Metrics

TL;DR

This paper introduces a novel single-pass online algorithm for association rule mining in hierarchical item classifications, optimizing processing efficiency and integrating Boolean constraints, suitable for dynamic transaction streams.

Contribution

The paper presents a new hierarchical, single-pass algorithm for online association rule mining that incorporates Boolean constraints and optimizations like hierarchy-aware counting.

Findings

Efficient single-pass algorithm for hierarchical association rule mining

Effective integration of Boolean constraints into the mining process

Processing efficiency improved through hierarchy-aware counting and transaction reduction

Abstract

Several multi-pass algorithms have been proposed for Association Rule Mining from static repositories. However, such algorithms are incapable of online processing of transaction streams. In this paper we introduce an efficient single-pass algorithm for mining association rules, given a hierarchical classification amongest items. Processing efficiency is achieved by utilizing two optimizations, hierarchy aware counting and transaction reduction, which become possible in the context of hierarchical classification. This paper considers the problem of integrating constraints that are Boolean expression over the presence or absence of items into the association discovery algorithm. This paper present three integrated algorithms for mining association rules with item constraints and discuss their tradeoffs. It is concluded that the variation of complexity depends on the measure of DIT (Depth of Inheritance Tree) and NOC (Number of Children) in the context of Hierarchical Classification.