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Survey on Identifying the Attributes That Improve the Object Visibility


Affiliations
1 Computer Science and Engineering in Karunya University, Coimbatore, India
2 Computer Science and Engineering Department in Karunya University, Coimbatore, India
     

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The existing top k- retrieval algorithms help the users for searching and retrieving the needed products from the databases. But there is a different view for the problem i.e. how the sellers can identify the user preferred features for a product. The problem is to identify the best attributes so that the product is highly visible to the customers. In this paper, several solutions for the problem are considered. It includes exact and approximation algorithms. The exact algorithm is a maximal frequent item set mining algorithm. This algorithm uses random walk in Dualize and Advance algorithm as its foundation. The approximation algorithms are based on greedy heuristics. Two greedy heuristics are described for solving the problem. This greedy heuristics is a modification of the existing greedy algorithm for the attribute selection. Even though the problem considered is novel, this paper surveys on the above specified algorithms.

Keywords

Data Mining, Knowledge and Data Engineering Tools and Techniques, Marketing, Mining Methods and Algorithms, Retrieval Models.
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  • Survey on Identifying the Attributes That Improve the Object Visibility

Abstract Views: 241  |  PDF Views: 1

Authors

T. Neethu Mohan
Computer Science and Engineering in Karunya University, Coimbatore, India
Deepa Kanmani
Computer Science and Engineering Department in Karunya University, Coimbatore, India
G. Hemalatha
Computer Science and Engineering Department in Karunya University, Coimbatore, India

Abstract


The existing top k- retrieval algorithms help the users for searching and retrieving the needed products from the databases. But there is a different view for the problem i.e. how the sellers can identify the user preferred features for a product. The problem is to identify the best attributes so that the product is highly visible to the customers. In this paper, several solutions for the problem are considered. It includes exact and approximation algorithms. The exact algorithm is a maximal frequent item set mining algorithm. This algorithm uses random walk in Dualize and Advance algorithm as its foundation. The approximation algorithms are based on greedy heuristics. Two greedy heuristics are described for solving the problem. This greedy heuristics is a modification of the existing greedy algorithm for the attribute selection. Even though the problem considered is novel, this paper surveys on the above specified algorithms.

Keywords


Data Mining, Knowledge and Data Engineering Tools and Techniques, Marketing, Mining Methods and Algorithms, Retrieval Models.