Expert system for retrieval of documents using evolutionary approaches incorporating clustering

Classification is a central problem in the fields of data mining and machine learning. Using a training set of labeled instances, the task is to build a model (classifier) that can be used to predict the class of new unlabelled instances. Data preparation is crucial to the data mining process, and its focus is to improve the fitness of the training data for the learning algorithms to produce more effective classifiers. Searching for the frequent pattern within a specific sequence has become a much needed task in various sectors. Feature selection is selecting a subset of optimal features. Feature selection is being used in high dimensional data reduction and it is being used in several applications like medical, image processing, text mining, etc. In the existing work, unsupervised feature selection methods using Artificial Bee Colony Optimization Algorithm, Bat Algorithm and Ant Colony Optimization have been introduced. We have compared these three algorithms and concluded that Bat Algorithm proves to be better in performance than the rest. The proposed system will use a novel method to select subset of features from unlabelled data using Bat algorithm with one of the clustering algorithm and develop an expert information retrieval system.