A Novel Clustering Algorithm to Capture Utility Information in Transactional Data

Piyush Lakhawat, Mayank Mishra, Arun Somani

2016

Abstract

We develop and design a novel clustering algorithm to capture utility information in transactional data. Transactional data is a special type of categorical data where transactions can be of varying length. A key objective for all categorical data analysis is pattern recognition. Therefore, transactional clustering algorithms focus on capturing the information on high frequency patterns from the data in the clusters. In recent times, utility information for category types in the data has been added to the transactional data model for a more realistic representation of data. As a result, the key information of interest has become high utility patterns instead of high frequency patterns. To the best our knowledge, no existing clustering algorithm for transactional data captures the utility information in the clusters found. Along with our new clustering rationale we also develop corresponding metrics for evaluating quality of clusters found. Experiments on real datasets show that the clusters found by our algorithm successfully capture the high utility patterns in the data. Comparative experiments with other clustering algorithms further illustrate the effectiveness of our algorithm.

Paper Citation

in Harvard Style

Lakhawat P., Mishra M. and Somani A. (2016). A Novel Clustering Algorithm to Capture Utility Information in Transactional Data . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 456-462. DOI: 10.5220/0006092104560462

in Bibtex Style

@conference{kdir16,
author={Piyush Lakhawat and Mayank Mishra and Arun Somani},
title={A Novel Clustering Algorithm to Capture Utility Information in Transactional Data},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={456-462},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006092104560462},
isbn={978-989-758-203-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - A Novel Clustering Algorithm to Capture Utility Information in Transactional Data
SN - 978-989-758-203-5
AU - Lakhawat P.
AU - Mishra M.
AU - Somani A.
PY - 2016
SP - 456
EP - 462
DO - 10.5220/0006092104560462