Campus Units

Electrical and Computer Engineering

Document Type

Conference Proceeding

Conference

9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Publication Version

Published Version

Publication Date

2017

Journal or Book Title

Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Volume

1

First Page

123

Last Page

134

DOI

10.5220/0006590001230134

Conference Title

9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Conference Date

November 1-3, 2017

City

Funchal, Madeira, Portugal

Abstract

We strongly believe that the current Utility Itemset Mining (UIM) problem model can be extended with a key modeling capability of predicting future itemsets based on prior knowledge of clusters in the dataset. Information in transactions fairly representative of a cluster type is more a characteristic of the cluster type than the the entire data. Subjecting such transactions to the common threshold in the UIM problem leads to information loss. We identify that an implicit use of the cluster structure of data in the UIM problem model will address this limitation. We achieve this by introducing a new clustering based utility in the definition of the UIM problem model and modifying the definitions of absolute utilities based on it. This enhances the UIM model by including a predictive aspect to it, thereby enabling the cluster specific patterns to emerge while still mining the inter-cluster patterns. By performing experiments on two real data sets we are able to verify that our proposed predictive UIM problem model extracts more useful information than the current UIM model with high accuracy.

Comments

This proceeding is published as Piyush Lakhawat and Arun K. Somani, “A Clustering based Prediction Scheme for High Utility Itemsets.” In: Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, 123-134, 2017, Funchal, Madeira, Portugal. DOI: 10.5220/0006590001230134. Published in SCITEPRESS Digital Library. Posted with permission.

Copyright Owner

SCITEPRESS – Science and Technology Publications, Lda

Language

en

File Format

application/pdf

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