Documents clustering method based on Ants Algorithms

Ants Algorithms, particularly Ant Colony Optimization meta-heuristic, are universal, flexible and give possibility to scale because they are based on multi agent cooperation. The increase of demand for effective methods of big document collections management is sufficient stimulus to place the research on the new application of ant based systems in the area of text document processing. In this publication author presents the implementation of that technique in the documents clustering area – new documents clustering method. The aim of this document is to present the details of the ACO documents clustering method and detail results of experiments. 1 Ant Colony Optimization One of the topics that was deeply explored in the past by ethnologist was the understanding of mechanism how almost blind animals were able to find the shortest way from nest to a food. Comprehension of the way to achieve this task by nature was the first step to implement that solution in algorithms area. Main inspiration to create ACO meta-heuristic were researches and experiments done by Goss and Deneubourg [1]. Ants ( Linepithaema humile ) are the insects that live in the community called colony. The primary goal of ants is the survival of the whole colony. A single specimen is not essential, only bigger community may efficiently cooperate. Ants possess the ability of such efficient cooperation. It bases on work of many creatures, who evaluate one solution as a colony of cooperative agents. Individuals do not communicate directly. Each ant creates its own solution that contributes to the whole colony's solution [3]. The ability to find the shortest way between the source of the food and the ant-heel is a very important and interesting behavior of the ant colony. It has been observed that ants use the specific substance called pheromone to mark the route they have already gone through. When the first ant randomly chooses one route it leaves the specific amount of pheromone, which gradually evaporates. Next ants which are looking for the way, will, with greater probability, choose the route where they feel more pheromone and after that they leave their own pheromone there. This process is autocatalic – the more ants choose a specific way, the more attractive it stays for the others. Above, information bases mainly on Marco Dorigo publications. He is the one who most of all contributed to develop the research in the ant systems area. His publications are the biggest repository of ACO information [2] [3]. 2 ACO-based clustering method Noticed analogy between finding the shortest way by ants and finding documents most alike (the shortest way between documents), and in addition ability to use agents who construct their individual solutions as an element of the general solution, became the stimulus to begin research on using the ant based algorithms in the documents clustering process [4]. 2.1 Adopting the ACO concepts to documents clustering task For the needs of building an effective method of classifying the documents, it is necessary to make a choice of possible modification and adjusting of the concepts specific to real ants, so as could be effectively used to solve the problems connected with text mining.