Data Mining and Knowledge Engineering

E. Chandra ¹, V. P. Anuradha ²

Affiliations
1 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore, IN
2 Karpagam University, Coimbatore, IN

Abstract

Query processing is an essential task to obtain meaningful information in a data mining application. It needs to be optimized for its effective implementation in any application. A join operation of relational data base management system is one such technique that can optimize the query process efficiently. In a similar manner the join operation in a spatial data base management system can be utilized to optimize the query process. Join operation itself is accelerated by the implementation of join indexes further optimizing the query process. Efficient implementation of join indices is possible with such multidimensional indexing structures as R-trees, Grid files, Bi-partite graphs, Neighbourhood graphs, etc. An effective join processing algorithm in collusion with the join index enhances the query process further. A cost model with CPU cost, I/O cost, number of page and node accesses with a constraint of fixed buffer size has to be evaluated to check the feasibility of the join operation.

Keywords

Spatial Databases, Join Indices, Join Operation, Multidimensional Indexing Structures.

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E. Chandra ¹, P. Ajitha ¹

Affiliations
1 Department of Computer Science, D J Academy for Managerial Excellence, Coimbatore-32, IN

Abstract

Data mediated knowledge discovery is essential for any end users for value added decision making. Discerning vital, accurate and precise knowledge in the classification, various feature subsets are necessary. Apart from feature selection, processing and representation of data is also indispensable for analysis and implementation of any knowledge. Principal Component Analysis is the used for data pre-processing and representation of data. Eigen vectors, co variance matrix are estimated for distributed environment where local and global set are computed and evaluated. It reduces the dimensionality of data. RELIEF, CMIM and other feature selection methods are discussed here in this paper. On selecting the features may increase the classification accuracy and enhance classification and prediction.

Keywords

Feature Selection, Models, Distributed Data Mining, PCA, Classification, CMIM, mRMR, RELIEF.

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E. Chandra ¹, K. Nandhini ²

Affiliations
1 Department of Computer Applications, D. J. Academy for Managerial Excellence, Coimbatore-32, Tamilnadu, IN
2 Computer Science Department, Dr. N. G. P. Arts and Science College, Coimbatore-48, Tamilnadu, IN

Abstract

The ability to predicting the performance of a student is very essential task of all educational institutions. This will not be decided by using only the academic excellence of a student. The behaviors such as aptitude, attitude, communications, technological, interpersonally, problem solving ability etc., should be taken into care to predict the real excellence of a student. This form a heterogeneous dataset covering cross section of categorical, integer type data types etc. This has given rise to a high dimensional dataset which will hamper classification process. Since this is the task of prediction and mining the classification algorithms of data mining is used. The decision tree algorithms of classification are one of the fine grained methods to bring the more accuracy of prediction. The first phase of the work is collecting the wide cross section of atabase of values for attributes which are quite cross functional. The second phase plays vital role for effective classification by narrowing down by selection of predictive attributes. This phase is done by Feature Extraction techniques to reduce the high dimensional dataset in to a low dimensional dataset. The third phase applying the algorithms uses the Naive Bayes and tree induction of decision tree methods for actual classification of the data. The scalability of these methods has improved by perception based learning. Also, there is a school of thought that one can take up the classification and data mining without incorporating any Dimensionality reduction techniques like Feature Extraction. This work compare results obtained by the both process and study the performance of the Prediction accuracy. It is not that only the student domain can be used for excellence prediction. It can be applied for any kind of domain.

Keywords

Data Mining, Decision Tree, Feature Extraction, Performance Prediction.

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