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Karpagam, K.
- Enhanced Association Rule Mining Algorithm to Extract High Utility Itemsets from a Large Dataset
Abstract Views :167 |
PDF Views:2
Authors
Affiliations
1 H.H. The Rajah's College (Autonomous), Pudukkottai, Tamil Nadu, IN
2 Dept. of Computer Applications, Karpaga Vinayaga College of Engineering and Technology, Kancheepuram, Tamil Nadu, IN
1 H.H. The Rajah's College (Autonomous), Pudukkottai, Tamil Nadu, IN
2 Dept. of Computer Applications, Karpaga Vinayaga College of Engineering and Technology, Kancheepuram, Tamil Nadu, IN
Source
Data Mining and Knowledge Engineering, Vol 7, No 7 (2015), Pagination: 238-241Abstract
Data mining aims at bringing out the hidden information from a large data set using data mining techniques according to the requirements. Association rule mining identifies itemsets that occur frequently in data set and frames association rules by taking all items equally. But many differences exist among the items that play a vital role in decision making. By taking one or more values of items as utilities, the utility mining technique works on finding the itemsets with greater utilities. In the proposed paper we present a utility mining algorithm named IUM (Improved Utility Mining) algorithm that finds high utility itemsets and also low utility itemsets from a large data set and the experiments states that the proposed algorithm performs better than existing algorithms in case of running time.Keywords
Association Rules, Frequent Itemsets, Low Utility Itemset, High Utility Itemset.
- Comparative Analysis of Optimization Algorithms for Document Clustering
Abstract Views :240 |
PDF Views:3
Authors
K. Karpagam
1,
A. Saradha
2
Affiliations
1 Department of Master of Computer Application, Dr. Mahalingam College of Engineering & Technology, Pollachi, IN
2 Department of Computer science and Engineering, Institute of Road and Transport Technology, Erode., IN
1 Department of Master of Computer Application, Dr. Mahalingam College of Engineering & Technology, Pollachi, IN
2 Department of Computer science and Engineering, Institute of Road and Transport Technology, Erode., IN
Source
Data Mining and Knowledge Engineering, Vol 9, No 6 (2017), Pagination: 120-125Abstract
Document clustering or text clustering is an unsupervised technique and it is used to grouping the documents of same context. Document clustering algorithms are widely used in web searching engines to produce results relevant to a query. Today, the information in websites is growing in huge size and it leads to the process of managing, retrieve the required and updated information is a tedious task. Also necessary to obtain the exact information required by the user from the documents. Recently optimization algorithms are introduced and are applied to the clustering algorithms. The Genetic Algorithm and Cuckoo Search algorithms are meta-heuristic optimization algorithms and are used to obtain the optimum solutions. In this paper, Genetic Algorithm and Cuckoo Search algorithm based Domain-specific Keyword Similarity based Knowledgebase Creation algorithm are proposed to optimize the document clustering to answers the question answering system. The experimental were conducted on benchmark datasets and the performance was analyzed in terms of Precision, Recall, F1, Missrate, Fallout and Purity.
Keywords
Cuckoo Search, Document Clustering, Genetic Algorithm, Information Processing Knowledge Base, Text Mining.References
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