iFUM Improved Fast Utility Mining

Kannimuthu, S.; Premalatha, K.; Shankar, S.

doi:10.5120/3343-4602

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…Mining based on item-sets utility: Depending upon his circumstance of usage (Pillai and Vyas, 2010) as precised by the user a high utility item-set is the one with utility value larger than the minimum brink utility. A wide topic that wraps all features of economic utility in data mining (Kannimuthu et al, 2011) is known to be utility-based data mining. It includes the work in cost-sensitive education and dynamic learning as well as work on the recognition of uncommon events of high effectiveness value by itself.…”

Section: Mining Based On Item-sets Frequencymentioning

confidence: 99%

An Extensive Review of Significant Researches in Data Mining

Mathai¹

2014

RJASET

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An action that removes a few novel nontrivial data enclosed in large databases is defined as Data Mining. On noticing the statistical connections between the items that are more regular in the operation databases traditional data mining methods have spotlighted mostly. Numerous functions are using data mining in dissimilar fields like medical, marketing and so on commonly. Several methods and techniques have been extended for mine the in order from the databases. In this study, we provide a comprehensive survey and study of various methods in existence for item set mining based on the utility and frequency and association rule mining based research works and also presented a brief introduction about data mining and its advantages. Moreover we present a concise description about the Data Mining techniques, performance review and the instructions for future research.

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Section: Mining Based On Item-sets Frequencymentioning

confidence: 99%

An Extensive Review of Significant Researches in Data Mining

Mathai¹

2014

RJASET

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“…But a frequent itemset only reproduces the statistical correlation between items, and it does not reflect the semantic importance of the items. To overcome this limitation, Kannimuthu et al [4] have utilized a utility based itemset mining approach. Utility-based data mining is a broad topic that covers all aspects of economic utility in data mining.…”

Section: Related Workmentioning

confidence: 99%

Different Approaches for Frequent Itemset Mining

Nikam¹,

Deshpande²

2018

IJSRCSE

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Abstract-Data mining is the retrieval of hidden analytical information from huge databases, is a controlling new technology with great possible to help organizations as well as research focus on the mainly essential information in their data warehouses. Data mining tools forecast future development and performances, allowing businesses to create proactive, idea for decision making systems. Frequent Itemset Mining (FIM) is one of the traditional data mining problems in mainly of the data mining approaches. It requires very huge computations and input and output traffic capacity. Also resources like single processor's memory and CPU are very limited, which degrades the presentation of algorithm. In this research work system proposed one such distributed approach which will run on Hadoop cluster -one of the recent most popular distributed frameworks which basically focus on parallel processing. The proposed framework takes into account extends characteristics of the Apriori algorithm related to the frequent itemset invention and throughout a block-based partitioning uses a dynamic workload management. The algorithm greatly improves the performance and gets high scalability compared to the existing approaches.Proposed algorithm is implemented and tested on large scale datasets distributed system on heterogeneous cluster.

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“…The iFUM algorithm also scales well as the number of distinct items increases in the input database. [20,21,26] In 2012 Cheng Wei Wu, Bai-En Shie, Philip S. Yu, Vincent S. Tseng proposed Mining Top-K High Utility Itemsets. They proposed an efficient algorithm named TKU for mining top-k high utility itemsets from transaction databases.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…Market basket analysis has also been used to identify the purchase patterns of the costumer, which play a key role behind the inception and design of a product. [26] …”

Section: Introduction 11 Data Miningmentioning

confidence: 99%

Mining High Utility Itemsets from Large Dynamic Dataset by Eliminating Unusual Items

Chaudhari¹,

Verma²

2013

IJCA

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Utility-based data mining is a new research area interested in all types of utility factors in data mining processes [1]. The basic meaning of utility is the quantity sold, interest, importance & profitability of items to the users. Utility of items in a transaction database consists of two aspects:1. The importance of distinct or unique items, which is called external utility. 2.The importance of the items in the transaction, w is called as internal utility.Mining high utility itemsets from the databases is not an easy task. Pruning search space for high utility itemset mining is difficult because a superset of a low utility itemset may be a high utility itemset. Existing studies [2,4,9] applied overestimated methods to facilitate the mining performance of utility mining. In these methods, first we will get potential high utility itemsets, and then an additional database scan is performed for identifying their utilities. However, the existing methods often generate a huge candidate itemsets and the mining performance is degraded consequently. In this paper we proposed Eliminating Unusual Itemset by Eliminating item set which is low utility item set to reduce search space. Proposed methods not only reduce the number of candidate itemsets, but also significantly increase the performance of the mining process.

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iFUM Improved Fast Utility Mining

Cited by 9 publications

References 9 publications

An Extensive Review of Significant Researches in Data Mining

An Extensive Review of Significant Researches in Data Mining

Different Approaches for Frequent Itemset Mining

Mining High Utility Itemsets from Large Dynamic Dataset by Eliminating Unusual Items

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