Data Mining and Knowledge Engineering

Amit Thakkar ¹, Y. P. Kosta ²

Affiliations
1 Chandubhai S. Patel Institute of Technology, Changa, Gujarat, IN
2 Marwadi Group of Institutions, Rajkot, Gujarat, IN

Abstract

Most of today’s structured data is stored in relational databases. Thus, the task of learning from relational data has begun to receive significant attention in the literature. Unfortunately, most methods only utilize “flat” data representations. Thus, to apply these single-table data mining techniques, we are forced to incur a computational penalty by first converting the data into this “flat” form. As a result of this transformation, the data not only loses its compact representation but the semantic information present in the relations are reduced or eliminated. As an important task of multi-relational data mining, multi-relational classification can directly look for patterns that involve multiple relations from a relational database and have more advantages than propositional data mining approaches. According to the differences in knowledge representation and strategy, the paper addressed different kind of multi-relational classification approaches that are ILP-based, graph-based and relational database-based classification approaches and discussed each relational classification technology, their characteristics, the comparisons and several challenging researching problems in detail.

Keywords

Multi-Relational Data Mining, Multi-Relational Classification, Inductive Logic Programming (ILP), Graph, Selection Graph, Tuple ID Propagation.

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Nidhi Shah ¹, Amit Ganatra ¹, C. K. Bhensdadia ², Y. P. Kosta ¹

Affiliations
1 Department of Computer Engineering CIT-Changa, Gujarat, IN
2 Department of Computer Engineering, Dharmsinh Desai University Nadiad, Gujarat, IN

Abstract

Given the explosive rate of data deposition on the web; classification has become a complex and dynamic phenomenon. As classification complexity is continuing to grow, so is the need in direct proportion to designing and developing data mining algorithms & techniques. Classification is the most commonly applied data mining technique, a process of finding a set of models or functions that describes and distinguishes data classes, for the purpose of using it – so classification is a specialist with specialized skills, which is moving toward universality. A classification problem is considered as a supervised learning problem. The aim of the classification task is to discover a kind of relationship between the attributes (input) and class (output), so that the discovered knowledge can be used to predict the class of a new unknown object. Classification of the records or data is done based on the classification rules. Ant colony optimization is a method that derives its inspiration from real ants that forage for food by selecting the shortest path from multiple possible paths available to reach food. Thus merging the concept of Ant Colony Optimization (ACO) with data mining brings in a new approach to designing classification rule that will be helpful in extraction of information for a specialized dataset. In this paper a survey is done on Ant-miner algorithm for classification Rule extraction. The Ant miner algorithm extract classification rule from data using if-then-else pattern; similar to other traditional algorithm available for classification task or purposes. Extraction of classification Rule from data is an important task of data mining. We present, detailed description about the algorithm available for classification rule mining using Ant colony optimization. Variations to the ant colony based an Ant-miner algorithm is discussed along with the comparison of the algorithms with critical parameters like predictive accuracy, No. of Rules Discovered, No. of terms per No. of rules Discovered, using different data sets. Hence the paper will help to study various ant miner algorithms and comparison carried out will help the data miner to select and use algorithm according to need based on the specialized properties associated with the algorithm.

Keywords

Ant Colony Optimization (ACO), Classification, Data Mining.

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Kamal Sutaria ¹, Amit Ganatra ², Y. P. Kosta ¹, C. K. Bhensdadia ³, Kruti Khalpada ¹

Affiliations
1 Charotar University of Science and Technology, Changa, Gujrat, IN
2 Charotar University of Science and Technology Changa, Gujrat, IN
3 Department of Computer Engineering, Dharamsinh Desai University, Nadiad, Gujarat, IN

Abstract

Incremental Flexible Frequency Discretization (IFFD) is a recently proposed discretization approach for Naïve Bayes (NB).IFFD performs satisfactory by setting the minimal interval frequency for discretized intervals as a fixed number. In this paper, we first argue that this setting cannot guarantee that the selecting MinBinSize is on always optimal for all the different datasets. So the performance of Naïve Bayes is not good in terms of classification error. We thus proposed a sequential search method for NB: named Optimum Binning. Experiments were conducted on 4 datasets from UCI machine learning repository and performance was compared between NB trained on the data discretized by OB, IFFD, and PKID.

Keywords

Discretization, Naïve Bayes, Optimum Binning.

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