Manuscript Number : IJSRSET184225
FEA-HUIM: Fast and Efficient Algorithm for High Utility Item-Set Mining Using Novel Data Structure and Pruning Strategy
Authors(3) :-Suresh B. Patel, Mahendra N. Patel, Dr. S. M. Shah
The aim is to recognize the item sets from transaction databases that direct the high profit of the business. It identifies groups of items that are brought together that earn a high profit. It can help the owner to earn more by promoting the sales of high utility items, so High Utility mining has attracted significant attention from the researchers. A number of algorithms have been designed to mine high-utility item-sets using various approaches and various data structures. However, it is necessary to improve the existing methods in terms of execution time and memory consumption. All previous high utility item-set mining algorithms like two-phase, HUI-Miner, FHM, mHUI-Miner scan the database multiple times. From the observation that we identified the performance of the algorithms can be improved by reducing the database scanning frequency and cost. In previous algorithms like HUI-Miner and mHUI-Miner, performs a time-consuming utility lists join operation on item-sets. In this research we propose a novel data structure Item Utility Matrix with Index vector and efficient procedure to join the utility list. We also propose a transaction aggregation to reduce the size of utility list. Our proposed algorithm outperforms the previous methods in execution time required.
Suresh B. Patel
Data Mining, High Utility Item-set, Transaction Weighted Utility, Item Utility Matrix, Index Vector.
Publication Details
Published in :
Volume 4 | Issue 2 | January-February 2018 Article Preview
PG Scholar, Computer Engineering Department, Government Engineering College, Modasa, Gujarat, India
Mahendra N. Patel
PG Scholar, Computer Engineering Department, Government Engineering College, Modasa, Gujarat, India
Dr. S. M. Shah
Computer Engineering Department, Government Engineering College, Modasa, Gujarat, India
Date of Publication :
2018-01-20
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) :
138-144
Manuscript Number :
IJSRSET184225
Publisher : Technoscience Academy
Journal URL :
https://ijsrset.com/IJSRSET184225