Design of Search Engine Considering Top k High Utility Item Set (HUI) Mining and Defining the Recommendation and Point of Interest (POI)

Ms. Sanjana S. Shirsat, Prof. S. A. Joshi


Frequent Item sets discovery mechanism of items set that are frequently purchased together by customer. High Utility Item sets (HUIs) deals with discovery of an item set with high utility such as streaming analysis, market analysis, mobile computing and biomedicine. HUIs consider with particular attribute of the item set. HUIs mining is defined as qualitative representation of user preferences. The primary goal of mining is to discover hidden patterns and unexpected trends in data set with HUI and the secondary goal is to discover Top-k results. Point of Interest (POI) is to provide personalized recommendation of particular aspects such as restaurants, movie theaters. Recommendation depends upon user previous interest or previous search options. HUIs mining identifies item sets where its attribute satisfies threshold. According to the threshold, items categorized as High Utility Item sets and Low Utility item sets. Resulting candidate item sets further undergoes pruning techniques so as to reduce no. of candidate item set considering. Efficient framework can be designed for mining Top-k HUIs set where k is no. of HUIs to be mined and defining the recommendation and POI. HUI mining is used to find Top-k results to enhance decision making process. HUIs mining is useful for building powerful search engines to display Top-k results. It has many application like market analysis, stream analysis and biometric. Index Terms—Utility mining, high utility item sets, high utility item set mining, top-k, pattern mining, parallel mining, top-k high utility item set mining, Data mining ,frequent item set, transactional database.

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V. S. Tseng, C. Wu, P. Fournier-Viger, and P. S. Yu, Efficient algorithms

for mining the concise and lossless representation of high utility itemsets,

IEEE Trans. Knowl. Data Eng., vol. 27, no. 3, pp. 726739, Mar. 1, 2015.

Vincent S. Tseng, Bai-En Shie, Cheng-Wei Wu, and Philip S. Yu,

Efficient Algorithms for Mining High Utility Itemsets from Transactional

Databases IEEE Trans. Knowl. Data Eng., VOL. 25, 2013.

J. Liu, K. Wang, and B. Fung, Direct discovery of high utility itemsets

without candidate generation, in Proc. IEEE Int. Conf. Data Mining, 2012,

pp. 984989.

CR.Nandhini, Dr.N.Suguna Shrewd Technique for Mining High Utility

Item set via TKU and TKO Algorithm, International Journal of Computer

Science and Information Technologies, Vol. 6, 2015.

U. Yun and J. Kim, A fast perturbation algorithm using tree structure for

privacy preserving utility mining, Expert Syst. Appl., vol. 42, no. 3, pp.

, 2015.

R. Agrawal and R. Srikant, Fast algorithms for mining association rules,

in Proc. Int. Conf. Very Large Data Bases, 1994, pp. 487499.

C. Ahmed, S. Tanbeer, B. Jeong, and Y. Lee, Efficient tree structures for

high-utility pattern mining in incremental databases, IEEE Trans. Knowl.

Data Eng., vol. 21, no. 12, pp. 17081721, Dec.2009.


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