Modification of Density Based Spatial Clustering Algorithm for Large Database Using Naive´s Bayes´ Theorem

The DBSCALE algorithms are used for clustering of very large database. The clustering techniques are very proficient and also the rate of correctness is increases, but these algorithms suffered from noise and outlier problem. The noise data and outlier decreases the performance. For the minimization of noise and outlier we modified DBSCALE algorithm using Naïve´s Baye´s theorem. Naïve´s Baye´s Theorem is basically a probability based function. This function estimate the outlier cluster data and increase the correctness rate of algorithm on according to threshold value. According to this techniques, it compute maximum posterior hypothesis for the outlier data.