Integrating ontology with dataminig with a case of mushroom analysis

Abstract

Data mining is extract the knowledge/information from a large amount of data which stores in multiple heterogeneous database. Data mining provides various techniques. Here using machine learning algorithm such as Pre- process, Classifier, Cluster and Association rule for help to improve the Predictive accuracy. Here presently the real data in the Explorer window for Exploring a data and applied these techniques to real mushroom data for predicting a edible mushrooms. They are described in terms of physical characteristics. The mushroom dataset can be based it’s attributes. All of the attribute values were nominal. Each field has a set of letter as possible values. It consists of a set of records with 22 attributes and a class label, Poisonous (or) Edible. The classifier could very well be a safe way to determine which Mushrooms I can and I can’t Eat. Then considering clustering algorithm with some enhancements to aid in the process of identification of Mushroom characteristics. To applied a classify techniques for predicting a correctly classified and incorrectly classified instances. Association rule mainly used for finding the best rule. In this rule is used to making decision for identify the mushroom is edible or poisonous.