Advance Mining of Temporal High Utility Itemset

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Author(s)

Swati Soni 1,* Sini shibu 2

1. Department of computer science, Technocrats Institute of Technology Bhopal, India

2. H.O.D Department of computer science, Technocrats Institute of Technology Bhopal, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2012.04.04

Received: 14 Jun. 2011 / Revised: 7 Oct. 2011 / Accepted: 10 Dec. 2011 / Published: 8 Apr. 2012

Index Terms

utility mining, temporal high utility itemsets, data streams, association rules, stocks

Abstract

The stock market domain is a dynamic and unpredictable environment. Traditional techniques, such as fundamental and technical analysis can provide investors with some tools for managing their stocks and predicting their prices. However, these techniques cannot discover all the possible relations between stocks and thus there is a need for a different approach that will provide a deeper kind of analysis. Data mining can be used extensively in the financial markets and help in stock-price forecasting. Therefore, we propose in this paper a portfolio management solution with business intelligence characteristics. We know that the temporal high utility itemsets are the itemsets with support larger than a pre-specified threshold in current time window of data stream. Discovery of temporal high utility itemsets is an important process for mining interesting patterns like association rules from data streams. We proposed the novel algorithm for temporal association mining with utility approach. This make us to find the temporal high utility itemset which can generate less candidate itemsets.

Cite This Paper

Swati Soni, Prof. Sini shibu, "Advance Mining of Temporal High Utility Itemset", International Journal of Information Technology and Computer Science(IJITCS), vol.4, no.4, pp.26-32, 2012. DOI:10.5815/ijitcs.2012.04.04

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