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Association Rule Mining based on Apriori Algorithm in Minimizing Candidate Generation
"... Abstract — Association Rule Mining is an area of data mining that focuses on pruning candidate keys. An Apriori algorithm is the most commonly used Association Rule Mining. This algorithm somehow has limitation and thus, giving the opportunity to do this research. This paper introduces a new way in ..."
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Abstract — Association Rule Mining is an area of data mining that focuses on pruning candidate keys. An Apriori algorithm is the most commonly used Association Rule Mining. This algorithm somehow has limitation and thus, giving the opportunity to do this research. This paper introduces a new way in which the Apriori algorithm can be improved. The modified algorithm introduces factors such as set size and set size frequency which in turn are being used to eliminate non significant candidate keys. With the use of these factors, the modified algorithm introduces a more efficient and effective way of minimizing candidate keys. Index Terms — Apriori algorithm, data mining, frequent items, set size
Optimized Frequent Pattern Mining for Classified Data Sets
, 2010
"... Mining frequent patterns in data is a useful requirement in several applications to guide future decisions. Association rule mining discovers interesting relationships among a large set of data items. Several association rule mining techniques exist, with the Apriori algorithm being common. Numerous ..."
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Mining frequent patterns in data is a useful requirement in several applications to guide future decisions. Association rule mining discovers interesting relationships among a large set of data items. Several association rule mining techniques exist, with the Apriori algorithm being common. Numerous algorithms have been proposed for efficient and fast association rule mining in data bases, but these seem to only look at the data as a set of transactions, each transaction being a collection of items. The performance of the association rule technique mainly depends on the generation of candidate sets. In this paper we present a modified Apriori algorithm for discovering frequent items in data sets that are classified into categories, assuming that a transaction involves maximum one item being picked up from each category. Our specialized algorithm takes less time for processing on classified data sets by optimizing candidate generation. More importantly, the proposed method can be used for a more efficient mining of relational data bases.
A Survey on Frequent Itemset Mining with Association Rules
 JOURNAL OF COMPUTER APPLICATIONS
, 2012
"... Data mining techniques comprises of Clustering, Association, Sequential mining, Classification, Regression and Deviation detection Association Rule mining is one of the utmost ubiquitous data mining techniques which can be defined as extracting the interesting correlation and relation among huge amo ..."
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Data mining techniques comprises of Clustering, Association, Sequential mining, Classification, Regression and Deviation detection Association Rule mining is one of the utmost ubiquitous data mining techniques which can be defined as extracting the interesting correlation and relation among huge amount of transactions. Many applications engender colossal amount of operational and behavioral data. Copious effective algorithms are proposed in the literature for mining frequent itemsets and association rules. Integrating efficacy considerations in data mining tasks is reaping popularity in recent years. Business value is enhanced by certain association rules and the data mining community has acknowledged the mining of these rules of interest since a long time. The discovery of frequent itemsets and association rules from transaction databases has aided many business applications. To discover the concealed knowledge from these data association rule mining can be applied in any application. A comprehensive analysis, survey and study of various approaches in existence for frequent itemset extraction, association rule mining with efficacy contemplations have been presented in this paper.
Ontological Frequent Patterns Mining by potential use of Neural Network
"... Association rule mining has attracted wide attention in both research and application areas recently. The mining of multilevel association rules is one of the important branches of it. Mining association rules at multiple levels helps in finding more specific and relevant knowledge.In most of the st ..."
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Association rule mining has attracted wide attention in both research and application areas recently. The mining of multilevel association rules is one of the important branches of it. Mining association rules at multiple levels helps in finding more specific and relevant knowledge.In most of the studies, multilevel rules will be mined through repeated mining from databases or mining the rules at each individually levels, it affects the efficiency, integrality and accuracy. In this paper, an efficient algorithm named Multi Level Feed Forward Mining (MLFM) is proposed for efficient mining of multiplelevel association rules from large transaction databases. This algorithm uses Feed Forward Neural Networks as Neural networks have been successfully applied in a wide range of supervised and unsupervised learning applications. neural networks have high acceptance ability for noisy data and high accuracy and are preferable in data mining. So we have used supervised neural network in parallel for finding frequent itemsets at each concept levels in only single scan of database. General Terms Data mining, association rules, multiplelevel association rules, support, confidance.
An effective model for improving the quality of recommender systems in mobile etourism
 International Journal of Computer Science & Information Technology (IJCSIT) Vol 4, No 1, Feb 2012 DOI : 10.5121/ijcsit.2012.4107 83
"... ABSTRACT In major ecommerce recommendation systems, the number of users and items is very ..."
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ABSTRACT In major ecommerce recommendation systems, the number of users and items is very
Fast Efficient Clustering Algorithm for Balanced Data
"... Abstract—The Cluster analysis is a major technique for statistical analysis, machine learning, pattern recognition, data mining, image analysis and bioinformatics. Kmeans algorithm is one of the most important clustering algorithms. However, the kmeans algorithm needs a large amount of computation ..."
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Abstract—The Cluster analysis is a major technique for statistical analysis, machine learning, pattern recognition, data mining, image analysis and bioinformatics. Kmeans algorithm is one of the most important clustering algorithms. However, the kmeans algorithm needs a large amount of computational time for handling large data sets. In this paper, we developed more efficient clustering algorithm to overcome this deficiency named Fast Balanced kmeans (FBKmeans). This algorithm is not only yields the best clustering results as in the kmeans algorithm but also requires less computational time. The algorithm is working well in the case of balanced data. Keywords—Clustering; Kmeans algorithm; Bee algorithm; GA algorithm; FBKmeans algorithm
Efficient Method for MultipleLevel Association Rules in Large Databases
"... The problems of developing models and algorithms for multilevel association mining pose for new challenges for mathematics and computer science. These problems become more challenging when some form of uncertainty in data or relationships in data exists. In this paper, we present a partition techniq ..."
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The problems of developing models and algorithms for multilevel association mining pose for new challenges for mathematics and computer science. These problems become more challenging when some form of uncertainty in data or relationships in data exists. In this paper, we present a partition technique for the multilevel association rule mining problem. Taking out association rules at multiple levels helps in discovering more specific and applicable knowledge. Even in computing, for the number of occurrence of an item, we require to scan the given database a lot of times. Thus we used partition method and boolean methods for finding frequent itemsets at each concept levels which reduce the number of scans, I/O cost and also reduce CPU overhead. In this paper, a new approach is introduced for solving the abovementioned issues. Therefore, this algorithm above all fit for very large size databases. We also use a topdown progressive deepening method, developed for efficient mining of multiplelevel association rules from large transaction databases based on the Apriori principle.
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"... Abstract Word sense disambiguation (WSD) is defined as the task of assigning the appropriate meaning (sense) to a given word in a text or discourse. The sense in which the word is used can be determined, most of the times, by the context in which the word occurs. Word sense ambiguity is a centra ..."
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Abstract Word sense disambiguation (WSD) is defined as the task of assigning the appropriate meaning (sense) to a given word in a text or discourse. The sense in which the word is used can be determined, most of the times, by the context in which the word occurs. Word sense ambiguity is a central problem for many established Human Language Technology applications (e.g., machine translation, information extraction, question answering, information retrieval, text classification, and text summarization). The context of an ambiguous word is regarded as a transaction record, the words in the context and the senses of the ambiguous word are regarded as items. If some items frequently occur together in some transactions (the context of the ambiguous word), then there must be some correlation between the items. The basic idea of the WSD algorithm based on mining association rules is: to discover the frequent item sets composed of the sense of the ambiguous word and its context by scanning its context database, which support degree is no less than the threshold of support degree; to produce the association rules X=>Y which confidence degree is no less than the threshold of the confidence degree from maximum frequent item sets; at last to determine the sense of the ambiguous word by choosing the sense which the most association rules deduced.
Mining Efficient Association Rules Through Apriori Algorithm Using Attributes 1
"... In data mining a number of algorithms has been proposed. Each algorithm has a different objective. A lot of research has been done on these various data mining fields and algorithms. Extraction of valuable data from large dataset is an emerging problem. Apriori algorithm is the algorithm to extract ..."
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In data mining a number of algorithms has been proposed. Each algorithm has a different objective. A lot of research has been done on these various data mining fields and algorithms. Extraction of valuable data from large dataset is an emerging problem. Apriori algorithm is the algorithm to extract association rules from dataset. Apriori algorithm is not an efficient algorithm as it is a time consuming algorithm in case of large dataset. With the time a number of changes proposed in Apriori to enhance the performance in term of time and number of database passes. This paper illustrate the apriori algorithm disadvantages and utilization of attributes which can improve the efficiency of apriori algorithm.
Performance Analysis of Apriori Algorithm with Progressive Approach for Mining Data
"... The Data Mining refers to extract or mine knowledge from huge volume of data. Association Rule mining is the technique for knowledge discovery. It is a wellknown method for discovering correlations between variables in large databases. One of the most famous association rule learning algorithm is A ..."
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The Data Mining refers to extract or mine knowledge from huge volume of data. Association Rule mining is the technique for knowledge discovery. It is a wellknown method for discovering correlations between variables in large databases. One of the most famous association rule learning algorithm is Apriori. The Apriori algorithm is based upon candidate set generation and test method. The problem that always appears during mining frequent relations is its exponential complexity. In this paper, we propose a new algorithm named progressive APRIORI (PAPRIORI) that will work rapidly`. This algorithm generates frequent itemsets by means of reading a particular set of transactions at a time while the size of original database is known.