Data stream clustering by divide and conquer approach based on vector model

Recently, many researchers have focused on data stream processing as an efficient method for extracting knowledge from big data. Data stream clustering is an unsupervised approach that is employed for huge data. The continuous effort on data stream clustering method has one common goal which is to achieve an accurate clustering algorithm. However, there are some issues that are overlooked by the previous works in proposing data stream clustering solutions; (1) clustering dataset including big segments of repetitive data, (2) monitoring clustering structure for ordinal data streams and (3) determining important parameters such as k number of exact clusters in stream of data. In this paper, DCSTREAM method is proposed with regard to the mentioned issues to cluster big datasets using the vector model and k-Means divide and conquer approach. Experimental results show that DCSTREAM can achieve superior quality and performance as compare to STREAM and ConStream methods for abrupt and gradual real world datasets. Results show that the usage of batch processing in DCSTREAM and ConStream is time consuming compared to STREAM but it avoids further analysis for detecting outliers and novel micro-clusters.


Springer Series in the Data Sciences

Springer Series in the Data Sciences focuses primarily on monographs and graduate level textbooks. The target audience includes students and researchers working in and across the fields of mathematics, theoretical computer science, and statistics. Data Analysis and Interpretation is a broad field encompassing some of the fastest-growing subjects in interdisciplinary statistics, mathematics and computer science. It encompasses a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, including diverse techniques under a variety of names, in different business, science, and social science domains. Springer Series in the Data Sciences addresses the needs of a broad spectrum of scientists and students who are utilizing quantitative methods in their daily research. The series is broad but structured, including topics within all core areas of the data sciences. The breadth of the series reflects the variation of scholarly projects currently underway in the field of machine learning.
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