Clustering and Classification with Non-Existence Attributes: A Sentenced Discrepancy Measure Based Technique

For some or all of the data instances a number of independent-world clustering issues suffer from incomplete data characterization due to losing or absent attributes. Typical clustering approaches cannot be applied directly to such data unless pre-processing by techniques like imputation or marginalization. We have overcome this drawback by utilizing a Sentenced Discrepancy Measure which we refer to as the Attribute Weighted Penalty based Discrepancy (AWPD). Using the AWPD measure, we modified the K-MEANS++ and Scalable K-MEANS++ for clustering algorithm and k Nearest Neighbor (kNN) for classification so as to make them directly applicable to datasets with non-existence attributes. We have presented a detailed theoretical analysis which shows that the new AWPD based K-MEANS++, Scalable K-MEANS++ and kNN algorithm merge into a local prime among the number of iterations is finite. We have reported in depth experiments on numerous benchmark datasets for various forms of Non-Existence showing that the projected clustering and classification techniques usually show better results in comparison to some of the renowned imputation methods that are generally used to process such insufficient data. This technique is designed to trace invaluable data to: directly apply our method on the datasets which have Non-Existence attributes and establish a method for detecting unstructured Non-Existence attributes with the best accuracy rate and minimum cost.

A Hybrid Algorithm Based Robust Big Data Clustering for Solving Unhealthy Initialization, Dynamic Centroid Selection and Empty clustering Problems with Analysis

Big Data is a massive volume of both structured and unstructured data that is too large and it also difficult to process using traditional techniques. Clustering algorithms have developed as a powerful learning tool that can exactly analyze the volume of data that produced by modern applications. Clustering in data mining is the grouping of a particular set of objects based on their characteristics. The main aim of clustering is to classified data into clusters such that objects are grouped in the same clusters when they are corresponding according to similarities and features mainly. Till now, K-MEANS is the best utilized calculation connected in a wide scope of zones to recognize gatherings where cluster separations are a lot than between gathering separations. Our developed algorithm works with K-MEANS for high quality clustering during clustering from big data. Our proposed algorithm EG K-MEANS : Extended Generation K-MEANS solves mainly three issues of K-MEANS: unhealthy initialization, dynamic centroid selection and empty clustering. It ensures the best way of preventing unhealthy initialization, dynamic centroid selection and empty clustering problems for getting high quality clustering.