9783659110320 - data mining-approaches to mine frequent patterns de gupta, bharat (4 resultados)
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Taschenbuch. Condición: Neu. Data Mining-Approaches to Mine Frequent Patterns | Data Mining Strategies for Transactional Databases Containing Maximal Frequent Patterns | Bharat Gupta | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783659110320 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengerich…er Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gupta BharatEr.Bharat Gupta has obtained his M.Tech degree from Thapar University, Punjab (India). He is a member of IEEE. He is working as assistant professor in engineering co…llege. He is the author of several papers published in i.
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In data mining, Association rule mining becomes one of the important tasks of descriptive technique which can be defined as discovering meaningful patterns from large collection of data. Mining frequent itemset is very fundamental part o…f association rule mining. Many algorithms have been proposed from last many decades including horizontal layout based techniques, vertical layout based techniques, and projected layout based techniques. But most of the techniques suffer from repeated database scan, Candidate generation (Apriori Algorithms), memory consumption problem (FP-tree Algorithms) and many more for mining frequent patterns. As in retailer industry many transactional databases contain same set of transactions many times, to apply this thought, in this thesis present a new technique which is combination of present maximal Apriori (improved Apriori) and FP-tree techniques that guarantee the better performance than classical aprioi algorithm. Another aim is to study and analyze the various existing techniques for mining frequent itemsets and evaluate the performance of new techniques and compare with the existing classical Apriori and FP- tree algorithm.
