Technological advancement has enabled us to store and process huge amount of data in relatively short spans of time. The nature of data is rapidly changing, particularly its dimensionality is more commonly multi- and high-dimensional. There is an immediate need to expand our focus to include analysis of high-dimensional and large datasets. Data analysis is becoming a mammoth task, due to incremental increase in data volume and complexity in terms of heterogony of data. It is due to this dynamic computing environment that the existing techniques either need to be modified or discarded to handle new data in multiple high-dimensions. Data clustering is a tool that is used in many disciplines, including data mining, so that meaningful knowledge can be extracted from seemingly unstructured data. The aim of this article is to understand the problem of clustering and various approaches addressing this problem. This article discusses the process of clustering from both microviews (data treating) and macroviews (overall clustering process). Different distance and similarity measures, which form the cornerstone of effective data clustering, are also identified. Further, an in-depth analysis of different clustering approaches focused on data mining, dealing with large-scale datasets is given. These approaches are comprehensively compared to bring out a clear differentiation among them. This article also surveys the problem of high-dimensional data and the existing approaches, that makes it more relevant. It also explores the latest trends in cluster analysis, and the real-life applications of this concept. This survey is exhaustive as it tries to cover all the aspects of clustering in the field of data mining.

Technological advancement has enabled us to store and process huge amount of data items in a relatively much lesser span of time. The term "Big Data" simply refers to huge amount of data nowadays used frequently in industrial and research circles. The focus point here is not just the collection of data but careful analysis of the collected data so that meaningful results can be obtained. There are various ways of handling the huge incoming streams of data. One such way is clustering of data into compact units. This not only reduces the size of the data but also helps to utilize it in a more effective manner. This paper gives an overview and comparison of basic clustering algorithms, and suggests the suitability of clustering approaches for various sizes of data sets.A brief introduction to evolution of the clustering algorithms is also given.

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Correlation determination brings out relationships in data that had not been seen before and it is imperative to successfully use the power of correlations for data mining. In this paper, we have used the concepts of correlations to cluster data, and merged it with recommendation algorithms. We have proposed two correlation clustering algorithms (RBACC and LGBACC), that are based on finding Spearman's rank correlation coefficient among data points, and using dimensionality reduction approach (PCA) along with graph theory respectively, to produce high quality hierarchical clusters. Both these algorithms have been tested on real life data (New York yellow cabs dataset taken from http://www.nyc.gov), using distributed and parallel computing (Spark and R). They are found to be scalable and perform better than the existing hierarchical clustering algorithms. These two approaches have been used to replace similarity measures in recommendation algorithms and generate a correlation clustering based recommendation system model. We have combined the power of correlation analysis with that of prediction analysis to propose a D.