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Ant Colony System (ACS) is competitive with other nature-inspired algorithms on some relatively simple problems. This project proposes an ant colony optimization algorithm for tuning generalization of fuzzy rule. The use of Ant Colony Optimization (ACO) for classification is investigated in depth, with the development of the AntMiner+ algorithm. AntMiner+ builds rule based classifiers, with a focus on the predictive accuracy and comprehensibility of the final models. The key differences between the proposed AntMiner+ and previous AntMiner versions are the usage of the better performing MAX-MIN ant system, a clearly defined and augmented environment for the ants to walk through, with the inclusion of the class variable to handle multiclass problems, and the ability to include interval rules in the rule list. Ant system is a general purpose algorithm inspired by the study of behavior of ant colonies. It is based on cooperative search paradigm that is applicable to the solution of combinatorial optimization problem. The institutions concern the routing network studies the application of data mining techniques for network traffic risk analysis. The proposed work aims at spatial feature of the traffic load and demand requirements and their interaction with the geo routing environment. In previous work, the system has implemented some spatial data mining methods such as generalization and characterization. The proposal of this work uses intelligent ant agent to evaluate the search space of the network traffic risk analysis along with usage of genetic algorithm for risk pattern.

Uncertainty Reduction in Data mining Task using Fuzzy based Integrated ACO and GA