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Authors
Affiliations
1 Computer Science and Engineering in Karunya
University, Coimbatore, IN
2 Computer Science and Engineering Department in Karunya University, Coimbatore, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 2 (2011), Pagination: 71-75
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.
