PERFORMANCE ENHANCEMENT OF DISTANCED BASED ALGORITHMS FOR CLASSIFICATION PROCESS

Nowadays there is vast amount of data being collected and stored in databases and without automatic methods for extracting this information it is practically impossible to mine for them. In Data Mining, the Classification processes perchance the most recognizable and most popular concepts. Actually Classification maps data into predefined groups or classes. Classification normally uses prediction rules to express knowledge. This Prediction rules are expressed in the form of IF - THEN rules. Sometimes this classification referred to as supervised learning by reason of the classes is determined before examining the data. Classification consists of predicting a certain outcome based on a given input. In order to predict the outcome, the algorithm processes a training set containing a set of attributes and the respective outcome, usually called goal or prediction attribute. Classification problems handled by using some known type of classification algorithms such that StatisticalBased Algorithms, Distance Based Algorithms etc,. All approaches to performing classification assume some knowledge of the data. This paper will focus on Distance Based Algorithms in classification. In Distance Based Algorithms each item is mapped to the same class may be thought of as more similar to the other items in that class than items found in other classes. Therefore, similarity or distance measures may be used to identify the "alikeness" of different items in the database. Using a similarity measure for classification where the classes are predefined is somewhat simpler than using a similarity measure for clustering where the classes are not known in advance. So the classification problem then becomes one of determining similarity not among all tuples in the database but between each tuple and the query