Data Mining-Approaches to Mine Frequent Patterns: Data Mining Strategies for Transactional Databases Containing Maximal Frequent Patterns

 
9783659110320: Data Mining-Approaches to Mine Frequent Patterns: Data Mining Strategies for Transactional Databases Containing Maximal Frequent Patterns

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 of 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.

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About the Author:

Er.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 college. He is the author of several papers published in international journals. His areas of interest are algorithm design and complexity issues for data mining.

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Book Description Book Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | Data Mining Strategies for Transactional Databases Containing Maximal Frequent Patterns | 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 of 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. | Format: Paperback | Language/Sprache: english | 64 pp. Bookseller Inventory # K9783659110320

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Book Description LAP Lambert Academic Publishing Apr 2012, 2012. Taschenbuch. Book Condition: Neu. Neuware - 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 of 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. 64 pp. Englisch. Bookseller Inventory # 9783659110320

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Book Description LAP Lambert Academic Publishing Apr 2012, 2012. Taschenbuch. Book Condition: Neu. Neuware - 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 of 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. 64 pp. Englisch. Bookseller Inventory # 9783659110320

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Book Description LAP Lambert Academic Publishing, Germany, 2012. Paperback. Book Condition: New. Aufl.. Language: English . Brand New Book ***** Print on Demand *****.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 of 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. Bookseller Inventory # AAV9783659110320

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Book Description LAP Lambert Academic Publishing. Paperback. Book Condition: New. 64 pages. Dimensions: 8.7in. x 5.9in. x 0.1in.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 of 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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Bookseller Inventory # 9783659110320

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Book Description LAP Lambert Academic Publishing Apr 2012, 2012. Taschenbuch. Book Condition: Neu. This item is printed on demand - Print on Demand Neuware - 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 of 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. 64 pp. Englisch. Bookseller Inventory # 9783659110320

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