Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Incremental Learning with Respect to New Incoming Input Attributes
Neural Processing Letters
Multimedia Data Mining and Its Implications for Query Processing
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A self-adaptive migration model genetic algorithm for data mining applications
Information Sciences: an International Journal
An incremental approach to genetic-algorithms-based classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental learning methods with retrieving of interfered patterns
IEEE Transactions on Neural Networks
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The optimization of queries is critical in database management systems and the complexity involved in finding optimal solutions has led to the development of heuristic approaches. Answering data mining query involves a random search over large databases. Due to the enormity of the data set involved, model simplification is necessary for quick answering of data mining queries. In this paper, we propose a hybrid model using rough sets and genetic algorithms for fast and efficient query answering. Rough sets are used to classify and summarize the datasets, whereas genetic algorithms are used for answering association related queries and feedback for adaptive classification. Here, we consider three types of queries, i.e., select, aggregate and classification based data mining queries. Summary tables that are built using rough sets and analytical model of attributes are used to speed up select queries. Mining associations, building concept hierarchies and reinforcement of reducts are achieved through genetic algorithms. The experiments are conducted on three real-life data sets, which include KDD 99 Cup data, Forest Cover-type data and Iris data. The performance of the proposed algorithm is analyzed for both execution time and classification accuracy and the results obtained are good.