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Thakkar, Amit
- Learning Using Heterogeneous Classifier in Data Mining
Abstract Views :187 |
PDF Views:2
Authors
Affiliations
1 Chandubhai S Patel Institute of Technology Changa, Gujarat, IN
2 Chandubhai S Patel Institute of Technology, Changa, Gujarat, IN
1 Chandubhai S Patel Institute of Technology Changa, Gujarat, IN
2 Chandubhai S Patel Institute of Technology, Changa, Gujarat, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 13 (2011), Pagination: 788-792Abstract
Data Mining can be considered an analytic process designed to explore business or market data to search for consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. Data mining is useful for prediction. We can improve accuracy of different classifiers by combining various classifiers and taking their predictions. One such method is Stacking, an ensemble method in which a number of base classifiers are combined using one meta-classifier which learns their outputs. This enhances the benefits obtained by individual classifiers. This paper is a review work of different approaches proposed by various authors in their paper.Keywords
Ensemble of Classifiers, Bagging, Boosting, Staking, Troika.- Improved K-Means with Dimensionality Reduction Technique
Abstract Views :183 |
PDF Views:3
Authors
Affiliations
1 Charotar Institute of Technology Changa, Nadiad, Gujarat, IN
1 Charotar Institute of Technology Changa, Nadiad, Gujarat, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 12 (2011), Pagination: 722-725Abstract
Clustering is the process of finding groups of objects such that the objects in a group will be similar to one another and different from the objects in other groups. K-means is a well known partitioning based clustering technique that attempts to find a user specified number of clusters represented by their centroid. K-means clustering algorithm often does not work well for high dimension; hence, to improve the efficiency, we apply PCA, dimensionality reduction technique, on data set and obtain a reduced dataset containing possibly uncorrelated variables. The challenging task for any clustering method is to determine the number of clusters beforehand. To find the number of cluster, we apply EM method that finds number of clusters user should choose by determining a mixture of Gaussians that fit a given data set. Finally the experiment results shows that the use of techniques such as PCA and EM, improve the efficiency of K-means clustering.Keywords
Cluster, EM, K-Mean, PCA.- Comprehensive and Evolution Study Focusing Future Research Challenges in the Field of Multi Relational Data Mining Specific to Multi-Relational Classification Approaches
Abstract Views :216 |
PDF Views:2
Authors
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
1 Chandubhai S. Patel Institute of Technology, Changa, Gujarat, IN
2 Marwadi Group of Institutions, Rajkot, Gujarat, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 10 (2011), Pagination: 594-598Abstract
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.- Classification using Generalization Based Decision Tree Induction along with Relevance Analysis Based on Relational Database
Abstract Views :199 |
PDF Views:3
Authors
Affiliations
1 Charotar Institute of Technology Changa, Gujarat, IN
2 Charotar Institute of Technology, Changa, Gujarat, IN
1 Charotar Institute of Technology Changa, Gujarat, IN
2 Charotar Institute of Technology, Changa, Gujarat, IN