Data Mining and Knowledge Engineering

K. Jothimani ¹, Antony Selvadoss Thanamani ¹

Affiliations
1 Department of Computer Science, NGM College, Pollachi, IN

Abstract

The most significant tasks in data mining are the process of mining frequent itemsets over data streams. It should support the flexible trade-off between processing time and mining accuracy. The objective was to propose an effective algorithm which generates frequent itemsets in a very less time by avoiding multiple scans. In this paper, we present an improved algorithm ESWCA for mining frequent itemsets using sliding window model. The ESWCA algorithm processes on an on-line transactional data stream. In this approach, we handle continues transaction slides in a segment-based manner which produces the improved runtime and memory consumption. Also, by revising the fair-cutter in the novel algorithm, multiple scans of the entire datasets will be avoided. Our experiments show that our algorithm not only achieved effectively consumes less memory, but also runs in an efficient manner.

Keywords

Data Stream, Data-Stream Mining, Frequent Itemset, and Sliding Window.

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A. Kutralam ¹, Antony Selvadoss Thanamani ²

Affiliations
1 Department of Computer Applications, J.J. College of Arts and Science, Pudukkottai, Tamilnadu, IN
2 Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu, IN

Abstract

Data mining is the process of extracting hidden patterns from data. As more data is gathered, with the amount of data increasing every year, data mining is becoming an increasingly important tool to transform this data into information. We focus on APRIORI algorithm, a popular data mining technique and analyze the performance of linked list based implementation as a basis for mining frequent item sequences in a transactional database. This algorithm has given us new capabilities to identify associations in large data sets. But an important issue, still not sufficiently scanned, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. We work with some association rule hiding algorithms and examine their performances in order to analyze their time complexity and the impact that they have in the original database. We work a side effect – the number of new rules generated during the hiding process.

Keywords

Association Rule Mining, Apriori Algorithm, Privacy Issues, Hiding Strategies.

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G. Marimuthu ¹, Antony Selvadoss Thanamani ²

Affiliations
1 Department of Computer Science, Yadava College, Madurai, IN
2 Department of Computer Science, NGM College, Coimbatore, IN

Abstract

Knowledge Management System refers to a system for managing knowledge in organizations, supporting creation, capture, storage and dissemination of information. It can comprise a part of a knowledge Management initiative. Knowledge Management can be regarded as a form of representation at the results of action and reflection in Explicit form Since the concept of the Industry cluster was popularized by Porter in 1990, many countries try to improve the competitiveness through Industry sector. Not only companies who take part in the cluster but also academic institutes, government agencies, associations and supportive industries. The more actors involved in the cluster the more knowledge were distributed among member of cluster. Although many literatures about cluster explained how knowledge is important for the cluster development. But, there is no specific knowledge management methodology or system for the cluster. This study is concerned about knowledge exchange in the cluster by using knowledge engineering methodology to analyze, model and design Knowledge Management System (KMS). At the end of this study, KMS will be studied the possibilities of implementing in the handicraft cluster as our case study. This paper present methodology and primary result form knowledge engineering. Then the KMS architecture was proposed as the result of the preliminary study in this paper.

Keywords

Knowledge Management, Architecture, Web Mining, Industry Cluster.

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