Abstract
In this research study, we analyze the performance of bio inspired classification approaches by selecting Ant-Miners (Ant-Miner, cAnt_Miner, cAnt_Miner2 and cAnt_MinerPB) for the discovery of classification rules in terms of accuracy, terms per rule, number of rules, running time and model size discovered by the corresponding rule mining algorithm. Classification rule discovery is still a challenging and emerging research problem in the field of data mining and knowledge discovery. Rule based classification has become cutting edge research area due to its importance and popular application areas in the banking, market basket analysis, credit card fraud detection, costumer behaviour, stock market prediction and protein sequence analysis. There are various approaches proposed for the discovery of classification rules like Artificial Neural Networks, Genetic Algorithm, Evolutionary Programming, SVM and Swarm Intelligence. This research study is focused on classification rule discovery by Ant Colony Optimization. For the performance analysis, Myra Tool is used for experiments on the 18 public datasets (available on the UCI repository). Data sets are selected with varying number of instances, number of attributes and number of classes. This research paper also provides focused survey of Ant-Miners for the discovery of classification rules.
Highlights
Classification Rule Mining is a Data Mining approach which discovers a set of rules for predicting the class of unseen data
The remaining paper consists of the following sections; firstly the section II provides the related work of different approaches exploited for the discovery of classification rule mining, the section III introduces the Ant Colony Optimization, the section IV provides detailed collection of ACO based rule discovery approaches with their critical analysis and comparison, the section V provides comparative performance analysis of selective Ant-Miners on public data sets
There are various statistical and evolutionary approaches proposed for the classification rule discovery and mining of association rules like artificial neural networks, Support Vector Machine (SVM), Genetic Algorithm (GA) and Swarm Intelligence
Summary
Classification Rule Mining is a Data Mining approach which discovers a set of rules for predicting the class of unseen data. We are focusing on classification rule mining by using ant colony optimization This survey study provides variants of ACO based classification rule mining approaches that are known as Ant-Miners in the literature, with their comparative study and analysis. First is intensive comparative performance analysis of bio inspired; Ant Colony Optimization based algorithmic approaches exploited for the discovery of classification rule mining and second is focused survey of Ant-Miners for the discovery of classification rules. The remaining paper consists of the following sections; firstly the section II provides the related work of different approaches exploited for the discovery of classification rule mining, the section III introduces the Ant Colony Optimization, the section IV provides detailed collection of ACO based rule discovery approaches with their critical analysis and comparison, the section V provides comparative performance analysis of selective Ant-Miners on public data sets. Conclusion and future work is given in the section VI
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