An Effective algorithm for Spam Filtering and Cluster Formation
Kavitha Guda, , , ,
Affiliations Associate Professor, Department of Computer Science and Engineering.
K-means clustering algorithm is one of the most widely used partitioning algorithms used for grouping the
elements over spatiotemporal data. It is the fast, simple and can work with large datasets. It has some of the pitfalls
regarding Number of iterations are more due to clusters details not known at an initial stage. It can detect only spherical
clusters. Here we will propose a Hybrid K-Means clustering algorithm which will mostly work on the concept of splitting
dataset and reducing the number of iterations. It will inherit the some of the features from two revised K-means
algorithms. The advantage of separating more massive datasets is that handle easy, and the benefit of reducing
iterations leads the easy cluster formation in this way the efficiency of the traditional K-means clustering algorithm is
increased. Furthermore, we also proposed Naïve Bayes Algorithm for Email Spam Filtering on SPAMBASE Dataset.
Kavitha Guda, “An Effective algorithm for Spam Filtering
and Cluster Formation”, International Journal Of Computer Engineering In Research Trends, 3(12):659-666, December-2016.
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