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8 articles for “itemsets”
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A Study of Mining High Utility Itemset
Abstract: Cost effective exploitation of a transactional database refers to the procedure of choosing the transaction sets with most cost effective features that will improve overall incomes of a company. A plethora of data mining algorithms have been recommended in the past few years to focus on mining item sets with high utility value. The word utility is about that feature of item-sets. Thus, mining algorithms tries to find out all …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 2, Issue 3, 2015 · pp. 21–25 Read article
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Data Mining Made Simple by A priori Algorithm for Market Analysis
Abstract: Data mining and data warehousing are increasingly becoming popular among IT professionals, academics and researchers from different disciplines. Business enterprises, small, medium or large scale, are considering the deployment of a warehouse as a major step and as a matter of pride. Data mining or knowledge discovery in data bases combines the techniques from mathematics, statistics, algorithms and artificial intelligence to extract the knowledge. Data mining is a main phase …
Published in Current Trends in Information Technology · Vol. 3, Issue 3, 2013 · pp. 1–4 Read article
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A Novel Approach for Enhancing Direct Hashing and Pruning for Association Rule Mining
Abstract: ABSTRACTData Mining has been considered as a promising field in the intersection of databases, artificial intelligence and machine learning. Association rule mining has been one of the most popular data mining subjects, which can be simply defined as finding interesting rules from large collections of data. This paper introduces an enhanced hashing approach in discovering associations for largeitemsets. The proposed hashing approach scans the entire database only once using the …
Published in Journal of Computer Technology & Applications · Vol. 3, Issue 1, 2012 · pp. 1–8 Read article
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A Study of DATA Mining Algorithms
Abstract: The time needed for generating frequent patterns plays a vital role. Some algorithms are designed, considering solely the time issue. Our study includes depth analysis of algorithms and discusses some problems of generating frequent pattern from the varied algorithms. We have explored the unifying feature among the inner operating of assorted mining algorithms. The work yields a close analysis of the algorithms to elucidate the performance with normal dataset like …
Published in Journal of Advanced Database Management & Systems · Vol. 2, Issue 1, 2015 · pp. 22–26 Read article
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Effective Implementation of Apriori Algorithm to Develop a Suggestion System Based on Sales History Using Hadoop Environment
Abstract: AbstractThe need for comprehensive support system to analyze and predict the nature of the dynamic market based on the previous records is very vital in competent industries today. The data mining is a process of extracting implicit, previously unknown and potentially useful information from data. Mining is search for relationships and global patterns that exist in the large databases but that are hidden among vast amount of data. Since the …
Published in Journal of Advanced Database Management & Systems · Vol. 3, Issue 3, 2016 · pp. 8–16 Read article
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Implementing FP-Growth Algorithm using Map Reduce for Mining Association Rules
Abstract: Abstract: In mining frequent itemsets, one of most important algorithms is FP-growth. FP-growth proposes an algorithm to compress information needed for mining frequent itemsets in FP-tree and recursively constructs FP-trees to find all frequent itemsets. Map Reduce is a distributed processing framework where the application is divided into many fragments of work, each of which may be executed on any node on a cluster. The main objective of this paper …
Published in Journal of Advanced Database Management & Systems · Vol. 6, Issue 2, 2019 · pp. 18–29 Read article
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Parallelizing Apriori Algorithm using Open MP
Abstract: An Association rule mining used for finding frequent item sets thus generating rules from the frequent item sets. Finding frequent itemsets is more expensive in terms of CPU power and computing resources utilization. Apriori Algorithm is used for high dimensionality on massively large data sets. Parallelism reduces the time required for serial processing. Parallel computing can be applied for mining of association rules. Parallel apriori algorithm focus on parallelizing the …
Published in Recent Trends in Parallel Computing · Vol. 1, Issue 1, 2014 · pp. 1–5 Read article
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A Review of Constrained Association Rule Mining
Abstract: Market basket analysis is a topic of concern when Apriori was developed. By the time, the algorithms are evolving and focusing on reducing complexity, number of database scans, using certain checking to generate only useful rules. For generating rules, firstly by support value, algorithms can extract frequent itemsets. After specifying confidence certain rules are generated. Because of generating buying pattern, a large number of areas are using association rule as …
Published in Journal of Web Engineering & Technology · Vol. 2, Issue 2, 2015 · pp. 18–22 Read article