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Balasubramanian, R.
- Enhanced Association Rule Mining Algorithm to Extract High Utility Itemsets from a Large Dataset
Authors
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.
- A Survey in Health Care Data Using Data Mining Techniques
Authors
1 Bharathidasan University, Department of Computer Applications, Anna University, Tiruchirappalli, IN
2 J.J. College of Arts and Science, Affiliated to Bharathidasan University, Pudukkottai, IN
Source
Data Mining and Knowledge Engineering, Vol 7, No 2 (2015), Pagination: 99-103Abstract
Data mining is the process of analyzing the enormous set of data. Data mining techniques have been used in healthcare research and known to be effective. Medical data has much information that needs to be exploited in order to get intelligence on medical events. Medical information is various in range and very large in content and its size is voluminous that conventional diagnostic technique disclose very little of the potential conclusion. Medical data mining can help to obtain latent patterns or actionable knowledge. It plays a significant function can spot trends and anomalies in their data in healthcare organization and disclose invaluable knowledge which in turn more useful for the healthcare professionals for decision making. In this paper we survey the effectiveness of diverse techniques in data mining such as classification, clustering, association, regression. These techniques can be applied to medical data to recognize trends and profiles hidden in mounds of data which may be essential to effective treatment for patients, management of healthcare organization and clinical feature of healthcare. This survey also highlights healthcare domain, requisite of data mining in Medicare field, algorithms used in today's healthcare domains.Keywords
Data Mining, Contemporary Data Mining Techniques, Medical Data Mining.- An Estimation of Privacy in Incremental Data Mining
Authors
1 Department of Information Technology, Sathyabama University, Chennai, IN
2 Department of Computer Science and Engineering, St. Joseph’s College of Engineering, Chennai, IN
3 Tata Consultancy Services, Chennai, IN
Source
Data Mining and Knowledge Engineering, Vol 2, No 4 (2010), Pagination:Abstract
Data are values of qualitative or quantitative variables, belonging to a set of items. In recent years, advances in hardware technology have lead to an increase in the capability to store and record personal data about consumers and individuals. This has lead to concerns that the personal data may be misused for a variety of purposes. Data explains a business transaction, a medical record, bank details, educational details etc., Use of technology for data collection and analysis has seen an unprecedented growth in the last couple of decades. Such information includes private details, which the owner doesn’t want to disclose. Such data are the sources for data mining. Data mining gives us “facts” that are not obvious to human analysts of the data. When such sensitive data are given directly for mining, the security of the individual is highly affected. So the data are modified and presented for data mining. But the problem is that the altered data should also produce a similar mining result. This has lead an area called privacy preservation in datamining which is an intersection of data mining and information security. The fact in this area is the additional task which is used to implement the privacy degrades the performance of the data mining algorithm, which results in incorrect mining results. This crucial situation has led to the development of this paper which deals with the data metrics that determines the quality of the following existing privacy preserving algorithms viz., Correlation- aware Anonymization of High-dimensional Data (CAHD) [1], Privacy-Preserving Outlier Detection Through Random Nonlinear Data Distortion (PRND) [2], Privacy-Preserving Data Aggregation(PPDA) [3], Privacy-Preserving Incremental Data sets( PRID) [4] which defines various methods for implementing privacy in incremental data. Major metrics like data utility, privacy and computational time are considered for evaluation and their detailed performance is discussed.