Mining Method for Long Pattern from Database
Journal: International Journal of Science and Research (IJSR) (Vol.3, No. 8)Publication Date: 2014-08-05
Authors : Madhu Nashipudimath; Monali Deshmukh;
Page : 526-529
Keywords : Candidate pruning; frequent itemset; high utility itemset; utility mining; data mining;
- Population Dynamics of Mosquito Larvae in Village Ponds and its Correlation with Physico-Chemical Parameters
- Physico-Chemical Assessment of Natural Breeding Habitats of Mosquito Larvae in Outskirts of Dehradun City, Uttarakhand
- ANALYSIS OF PHYSICO-CHEMICAL PARAMETERS OF SOIL FROM TEROGVUNYU VILLAGE AND HENBENJI VILLAGE UNDER TSEMINYU DISTRICT, NAGALAND
- PHYSICO - CHEMICAL PARAMETERS FROM THE MANAPPADAIYUR AND SWAMIMALAI FRESH WATER PONDS
- STUDY OF PHYSICO-CHEMICAL PARAMETERS OF TWO PONDS IN DARBHANGA DISTRICT OF BIHAR
Abstract
Mining high utility item sets from a transactional database refers to the discovery of item sets with high utility like profits. Although a number of relevant algorithms have been proposed in recent years, they incur the problem of producing a large number of candidate item sets for high utility item sets. Such a large number of candidate item sets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility item sets. In this paper, we propose two algorithms, namely UP-Growth (Utility Pattern Growth) and UP-Growth+, for mining high utility item sets with a set of effective strategies for pruning candidate item sets. The information of high utility item sets is maintained in a tree-based data structure named UP-Tree (Utility Pattern Tree) such that candidate item sets can be generated efficiently with only two scans of database. The performance of UP-Growth and UP Growth+ is compared with the state-of-the-art algorithms on many types of both real and synthetic datasets. Experimental results show that the proposed algorithms, especially UP-Growth+, not only reduce the number of candidates effectively but also outperform other algorithms substantially in terms of runtime, especially when databases contain lots of long transactions.
Other Latest Articles
- Impact of Shift Timings and Income on Work Life Balance of Employees in Shipping Organization
- Analyze the Affective Factors of Job Satisfaction: A Case Study of Telecom Sector in India
- Graph-based Attack Detection in Cloud using KDD CUP 99 Dataset
- Serum Calcium Level in HIV Patients at Federal Medical Center Yenagoa, Bayelsa State, Nigeria
- Epidemiological Study of Road Traffic Fatalities: 5 Years Retrospective Autopsied Cases Study in Varanasi, Uttar Pradesh, India
Last modified: 2021-06-30 21:05:59