A Data Cleansing Method for Clustering Large-scale Transaction Databases

TL;DR

This paper introduces a novel data cleansing method designed to enhance clustering quality and efficiency for large-scale transaction databases, demonstrating significant improvements through experimental validation.

Contribution

The paper presents a new data cleansing technique specifically tailored for large-scale transaction data, significantly improving clustering results and computational performance.

Findings

Clustering quality improved by up to 165%.

Clustering performance increased by up to 330%.

Method validated through extensive experiments.

Abstract

In this paper, we emphasize the need for data cleansing when clustering large-scale transaction databases and propose a new data cleansing method that improves clustering quality and performance. We evaluate our data cleansing method through a series of experiments. As a result, the clustering quality and performance were significantly improved by up to 165% and 330%, respectively.