A Survival Study on Data Structure Based Clustering Techniques for Multidimensional Data Stream Analysis

The advent of geospatial big data has led to a paradigm shift where most related applications became data driven, and therefore intensive in both data and computation. This revolution has covered most domains, namely the real-time systems such as web search engines, social networks, and tracking systems. These later are linked to the high-velocity feature, which characterizes the dynamism, the fast changing and moving data streams. Therefore, the response time and speed of such queries, along with the space complexity, are among data stream analysis system requirements, which still require improvements using sophisticated algorithms. In this vein, this chapter discusses new approaches that can reduce the complexity and costs in time and space while improving the efficiency and quality of responses of geospatial big data stream analysis to efficiently detect changes over time, conclude, and predict future events.

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Data Stream Analysis

Advances in Big Data Analytics ◽

10.1007/978-981-16-3607-3_5 ◽

2022 ◽

pp. 305-333

Author(s):

Yong Shi

Keyword(s):

Data Stream ◽

Data Stream Analysis

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EKEGWO: Enhanced Kernel-Based Exponential Grey Wolf Optimizer for Bi-Objective Data Clustering

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488519500296 ◽

2019 ◽

Vol 27 (04) ◽

pp. 669-688 ◽

Cited By ~ 1

Author(s):

Amolkumar Narayan Jadhav ◽

Gomathi N.

Keyword(s):

Data Clustering ◽

Clustering Algorithm ◽

Fitness Function ◽

Multidimensional Data ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Widespread Application ◽

Clustering Techniques ◽

Cluster Distance ◽

F Measure

The widespread application of clustering in various fields leads to the discovery of different clustering techniques in order to partition multidimensional data into separable clusters. Although there are various clustering approaches used in literature, optimized clustering techniques with multi-objective consideration are rare. This paper proposes a novel data clustering algorithm, Enhanced Kernel-based Exponential Grey Wolf Optimization (EKEGWO), handling two objectives. EKEGWO, which is the extension of KEGWO, adopts weight exponential functions to improve the searching process of clustering. Moreover, the fitness function of the algorithm includes intra-cluster distance and the inter-cluster distance as an objective to provide an optimum selection of cluster centroids. The performance of the proposed technique is evaluated by comparing with the existing approaches PSC, mPSC, GWO, and EGWO for two datasets: banknote authentication and iris. Four metrics, Mean Square Error (MSE), F-measure, rand and jaccord coefficient, estimates the clustering efficiency of the algorithm. The proposed EKEGWO algorithm can attain an MSE of 837, F-measure of 0.9657, rand coefficient of 0.8472, jaccord coefficient of 0.7812, for the banknote dataset.

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