Data Mining and Knowledge Engineering

N. Kavitha ¹, S. Karthikeyan ²

Affiliations
1 Computer Science Department, Karpagam University, IN
2 Department of Information Technology, College of Applied Sciences, Sohar, OM

Abstract

Data mining is the process of extracting interesting and previously unknown patterns and correlation form huge data stored in data bases. Association rule mining-a descriptive mining technique of data mining is the process of discovering items or literals which tend to occur together in transactions. The problem of the data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consuming operation in this discovery process is the computation of the frequency of the occurrences of interesting subset of items. Most of the previous research based on Apriori, which suffers with generation of huge number of candidate item sets and performs repeated passes for finding frequent item sets. To address this problem, in the proposed algorithm for finding frequent K-item sets in which the database is not used at all for counting the support of candidate item sets after the first pass. This makes the size of the encoding much smaller than the database, thus saving much reading effort. The Experimental Results are included.

Keywords

Data Mining, Frequent Itemset, Apriori.

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G. Appasami ¹, S. Karthikeyan ²

Affiliations
1 Department of CSE at Dr. Pauls Engineering College, Chennai, IN
2 Department of IT at Bannariamman Institute of technology, Coimbatore, IN

Abstract

In this paper we present a framework to manage the distributed and heterogeneous databases in grid environment Using Open Grid Services Architecture – Data Access and Integration (ODSA-DAI). Even though there is a lot of improvement in database technology, connecting heterogeneous databases within a single application challenging task. Maintaining the information for future purpose is very important in database technology. Whenever the information is needed, then it refers the database, process query and finally produces the result. Database maintains the billion of information. User maintains their information in different database. So whenever they need, they collect it from different database. User cannot easily collect their information from different database without having database knowledge. The current database interfaces are just collecting the information from many databases. The Intelligent Knowledge Based Heterogeneous Database using OGSA-DAI Architecture (IKBHDOA) provides solution to the problem of writing query and knowing technical details of Database. It has intelligence to retrieve the information from Different Sets of Database based on user’s inputs.

Keywords

Intelligent Knowledge Base (IKB), Heterogeneous Database, Automatic Query Generation (AQG), Relationship Between Database Tables.

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R. Gunasundari ¹, S. Karthikeyan ¹

Affiliations
1 Karpagam University, Tamilnadu, IN

Abstract

The Internet is a huge collection of data that is highly unstructured which makes it enormously difficult to search and retrieve valuable information. The present day’s web searching capabilities, networking and computational efficiency has allowed the user with huge bandwidth and very fast downloading speeds, but the time wasted in browsing through the uninteresting documents is enormous. The unstructured characteristic of the information sources on the Web makes automated discovery of Web information difficult. The goal of the paper is to design a new method in the Web Content Mining category and to describe its prototype implementation and the first experiments. The proposed method concerns the problem and how to determine a relevance ranking of web pages with respect to a given query. 

Keywords

Content Mining Data Mining, Search Engines, Soft Computing, Web Mining.

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