Inhaltsangabe
Mining frequent patterns (itemsets) plays an important role of in discovering association rules. However, finding frequent itemsets is most expensive step in the process of association rule mining. Very often algorithms to find frequent itemsets need multiple database scans creating a bottle-neck to achieve efficiency. To avoid this bottle-neck the objective has been to reduce database scans. In the past, Apriori-like methods were adopted to mine frequent itemsets. But these approaches are inefficient as they require multiple database scans and iteratively check a large set of candidates by pattern matching. A compact structure, called FP-Tree, was developed to improve the disadvantages of Apriori-like algorithms. By FP-Growth approach, we can facilitate mining frequent itemsets. This book proposes an Improved FP-Growth algorithm that scans database only once for association rule mining. The original FP-Growth algorithm scans datasets twice. First time, scanning database to find the frequent 1-itemsets, and sorting the 1-itemsets in the descending order of support and second time it scans the database again to construct FP-tree.
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Über die Autorin bzw. den Autor
Mr. Kuldeep Singh is currently pursuing his PhD in the dept. of CSE from IIT (BHU) Varanasi, India. His research interest includes high utility pattern mining, social network analysis and data mining. He received his M.Tech degree in the dept. of CSE from GJU S & T, Hisar (Haryana), India. He has 3 years of teaching and research experience.
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Improved FP Growth Algorithm for Mining Association Rules
Verlag:
LAP LAMBERT Academic Publishing Jun 2017, 2017
ISBN 10: 3330341904
ISBN 13: 9783330341906
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Mining frequent patterns (itemsets) plays an important role of in discovering association rules. However, finding frequent itemsets is most expensive step in the process of association rule mining. Very often algorithms to find frequent itemsets need multiple database scans creating a bottle-neck to achieve efficiency. To avoid this bottle-neck the objective has been to reduce database scans. In the past, Apriori-like methods were adopted to mine frequent itemsets. But these approaches are inefficient as they require multiple database scans and iteratively check a large set of candidates by pattern matching. A compact structure, called FP-Tree, was developed to improve the disadvantages of Apriori-like algorithms. By FP-Growth approach, we can facilitate mining frequent itemsets. This book proposes an Improved FP-Growth algorithm that scans database only once for association rule mining. The original FP-Growth algorithm scans datasets twice. First time, scanning database to find the frequent 1-itemsets, and sorting the 1-itemsets in the descending order of support and second time it scans the database again to construct FP-tree. 56 pp. Englisch. Bestandsnummer des Verkäufers 9783330341906
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Singh KuldeepMr. Kuldeep Singh is currently pursuing his PhD in the dept. of CSE from IIT (BHU) Varanasi, India. His research interest includes high utility pattern mining, social network analysis and data mining. He received his M.T. Bestandsnummer des Verkäufers 156197849
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Improved FP Growth Algorithm for Mining Association Rules
Verlag:
LAP LAMBERT Academic Publishing Jun 2017, 2017
ISBN 10: 3330341904
ISBN 13: 9783330341906
Neu
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Mining frequent patterns (itemsets) plays an important role of in discovering association rules. However, finding frequent itemsets is most expensive step in the process of association rule mining. Very often algorithms to find frequent itemsets need multiple database scans creating a bottle-neck to achieve efficiency. To avoid this bottle-neck the objective has been to reduce database scans. In the past, Apriori-like methods were adopted to mine frequent itemsets. But these approaches are inefficient as they require multiple database scans and iteratively check a large set of candidates by pattern matching. A compact structure, called FP-Tree, was developed to improve the disadvantages of Apriori-like algorithms. By FP-Growth approach, we can facilitate mining frequent itemsets. This book proposes an Improved FP-Growth algorithm that scans database only once for association rule mining. The original FP-Growth algorithm scans datasets twice. First time, scanning database to find the frequent 1-itemsets, and sorting the 1-itemsets in the descending order of support and second time it scans the database again to construct FP-tree.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch. Bestandsnummer des Verkäufers 9783330341906
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ISBN 10: 3330341904
ISBN 13: 9783330341906
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Mining frequent patterns (itemsets) plays an important role of in discovering association rules. However, finding frequent itemsets is most expensive step in the process of association rule mining. Very often algorithms to find frequent itemsets need multiple database scans creating a bottle-neck to achieve efficiency. To avoid this bottle-neck the objective has been to reduce database scans. In the past, Apriori-like methods were adopted to mine frequent itemsets. But these approaches are inefficient as they require multiple database scans and iteratively check a large set of candidates by pattern matching. A compact structure, called FP-Tree, was developed to improve the disadvantages of Apriori-like algorithms. By FP-Growth approach, we can facilitate mining frequent itemsets. This book proposes an Improved FP-Growth algorithm that scans database only once for association rule mining. The original FP-Growth algorithm scans datasets twice. First time, scanning database to find the frequent 1-itemsets, and sorting the 1-itemsets in the descending order of support and second time it scans the database again to construct FP-tree. Bestandsnummer des Verkäufers 9783330341906
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Taschenbuch. Zustand: Neu. Improved FP Growth Algorithm for Mining Association Rules | Kuldeep Singh (u. a.) | Taschenbuch | 56 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330341906 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 109487123
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