An enhanced Method for performing clustering and detecting outliers using mapreduce in datamining

Existing studies in data mining focus on Outlier detection on data with single clustering algorithm mostly. There are lots of Clustering methods available in data mining. The values or objects that are similar to each other are organized in group it’s called cluster and the values or objects that do not comply with the model or general behavior of the data these data objects called outliers. Outliers detect by clustering. Many Algorithms have been developed for clustering. Where partitional and Hierarchical Clustering is the two well known methods for clustering. In comparison of Hierarchical and Partitional Clustering Majority of the Hierarchical algorithms are very computationally, complex and consume high memory space. Whereas majority of Partitional clustering algorithm have required a linear time with better effectiveness. The clustering quality is not as Better as that a Hierarchical clustering algorithm. Hierarchical and Partitional Clustering algorithm have advantage over each other so in our proposed algorithm we integrate the Partitional Algorithm K-Modes because of Categorial Dataset and Hierarchical Clustering Algorithm CURE because of Large dataset, robust to outliers and identified cluster having non-spherical shape. And we plan to implement that algorithm in MapReduce Framework so the execution time of the algorithm is improve.