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Advance Mining of Temporal High Utility Itemset 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. EXISTING SYSTEM The rationale behind mining frequent itemsets is that only itemsets with high frequency are of interest to users. However, the practical usefulness of frequent itemsets is limited by the significance of the discovered itemsets. A frequent itemset only reflects the statistical correlation between items, and it does not reflect the semantic significance of the items. In this paper, we propose a utility based itemsetmining approach to overcome this limitation. PROPOSED SYSTEM The rationale behind mining frequent itemsets is that only itemsets with high frequency are of interest to users. However, the practical usefulness of frequent itemsets is limited by the significance of the discovered itemsets. A frequent itemset only reflects the statistical correlation between items, and it does not reflect the semantic significance of the items. In this paper, we propose a utility based itemsetmining approach to overcome this limitation. The proposed approach permits users to quantify their preferences concerning the usefulness of itemsets using utility values. The usefulness of an itemset is characterized as a utility constraint. That is, an itemset is interesting to the user only if it satisfies a given utility constraint. We show that the pruning strategies used in previous itemsetmining approaches cannot be applied to utility constraints. In response, we identify several mathematical properties of utility constraints. Then, two novel pruning strategies are designed. Two algorithms for utility based itemsetmining are developed by incorporating these pruning strategies. The algorithms are evaluated by applying them to synthetic and real world databases. Experimental results show that the proposed algorithms are effective on the databases tested. MODULE DESCRIPTION: Number of Modules After careful analysis the system has been identified to have the following modules: 1. Stock Trading Module. 2. Temporal Data Mining Module. 3. Time series Module. 4. Principle Of Apriori Algorithm Module SYSTEM REQUIREMENT: Hardware Requirements • System : Pentium IV 2.4 GHz • Hard disk : 40 GB • Monitor : 15 VGA colour • Mouse : Logitech. • Ram : 256 MB • Keyboard : 110 keys enhanced. Software Requirements Operating System : Programming language: c#.Net Web-Technology: ASP Front-End: ASP.NET Back-End: SQL SERVER Windows