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The exponential advancement in digital and hardware technology over the last few decades has led to tremendous increase in the amount of data received and stored in various data warehouses and data repositories around the world. The data collection and handling is not of much concern but the useful and relevant information retrieval from the so called raw data is one area that has clearly seen loads of efforts coming into it recently.
Data Mining (DM) is one commonly used name to address the efforts put in to derive a set of high level knowledge, from data, which is said to be both comprehensible and accurate (Fayyad, Piatetsky-Shapiro, & Smyth, 1996). In other words we can say that DM is the process of automatically seeking, constructing, analyzing and validating the structural patterns in data and in turn using them, for prediction, on unseen data (Witten, Frank, 1999; Lu, Setiono, & Liu, 1995)
Decision trees (Quinlan, 1986), neural networks (Odom & Sharda, 1990) and genetic algorithms (Goldberg, 1989) are some of the data mining techniques employed to extract classification rules. Currently many classification algorithms are used to extract some kind of relevant pattern amongst the data in the form of a model which can be used in prediction phase for the classification of unseen data. These include decision tree (Quinlan, 1986), neural networks (Odom & Sharda, 1990), support vector machine (Vapnik, 1995). It is an undeniable fact that neural networks, support vector machine and statistical algorithms are strong when it comes to predictive accuracy but fall short in terms of simplicity and comprehensibility. It is nearly impossible to extract useful high level knowledge, from the model developed, which can be easily understood and comprehended by the user. Thus they are called ‘Black Box’. This black box nature is overcome by the new algorithms which generate a set of easily interpreted rules as the model itself.
Recently efforts have been put into research, with success, related to nature-based approaches for finding the solution to classification problems. These include Genetic Algorithms (GA) (Mahfoud & Mani, 1996), Ant Colony Optimization (ACO) (Dorigo & Maniezzo, 1996., Dorigo & Stutzle, 2004) and Particle Swarm Optimization (PSO) (Sousa, Silva, & Neves, 2003) etc. which fall under Swarm intelligence (SI).
Since we developed a new variant of ACO-Miner we discuss more on ACO in the following. The basic ACO technique consists of a population of ants (artificial) which searches in the solution space of the given problem, guided by the pheromone trails and a heuristic approach. The pheromone update and evaporation take place depending upon the past exposure and experience of the ants (candidate solutions). This eventually increases the probability of finding the optimum solution to the problem in the next iteration (Dorigo & Gambardella, 1997).. Its significance and efficiency in various fields has been successfully demonstrated by successful applications to hard combinatorial optimization problems (Dorigo & Stutzle, 2004). In this paper we developed an advanced ant colony optimization algorithm (ADACOM) which makes use of a different but efficient heuristic approach. The new algorithm makes use of Gini’s Index, instead of Entropy, as the measure of information level in a particular term. In addition to this the ADACOM tries to capture the overlooked information resulting from the parameter constraints. This is accomplished by dynamically varying the parameters so as to retrieve more knowledge from the data by compensating the simplicity of the rules extracted but significantly improving upon the predictive accuracy., Ripper, C4.5 (Quinlan, 1993), INN, logit (Ohlson, 1980) and SVM (Quinlan, 1986) are compared with ADACOM for the benchmark datasets.