Data Stream Clustering Techniques, Applications, and Models: Comparative Analysis and Discussion

Abstract

Data growth in today’s world is exponential, many applications generate huge amount of data streams at very high speed such as smart grids, sensor networks, video surveillance, financial systems, medical science data, web click streams, network data, etc. In the case of traditional data mining, the data set is generally static in nature and available many times for processing and analysis. However, data stream mining has to satisfy constraints related to real-time response, bounded and limited memory, single-pass, and concept-drift detection. The main problem is identifying the hidden pattern and knowledge for understanding the context for identifying trends from continuous data streams. In this paper, various data stream methods and algorithms are reviewed and evaluated on standard synthetic data streams and real-life data streams. Density-micro clustering and density-grid-based clustering algorithms are discussed and comparative analysis in terms of various internal and external clustering evaluation methods is performed. It was observed that a single algorithm cannot satisfy all the performance measures. The performance of these data stream clustering algorithms is domain-specific and requires many parameters for density and noise thresholds.

Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks

“…It also supports data aggregation by removing data redundancy before transmitting them to the sink to gain energy conservation. Recently, in [6,8,29,30], the authors have presented a comprehensive overview of various data clustering techniques for in-network data reduction in periodic WSNs. Generally, these techniques can be classified into three different categories as presented in Figure 1, which are feature extraction-based, model-based and feature selection-based data clustering.…”

Application of Electrical Capacitance Tomography for Imaging Conductive Materials in Industrial Processes

“…(1) The first approach is to cluster the available data at different values of K and afterward compute validity measures to evaluate the quality of the attained clusters. There are many scalar validity measures in the literature [38] (2) The second approach is to work with an appropriately large number of K, then repeatedly decrease this number by combining similar clusters based on some predefined measures…”

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

“…Although the literature is replete with examples of data clustering strategies based on DE, WOA and BAT for the static domain, as, e.g., those presented in [13][14][15][16], little has been done for the dynamic environment due to the difficulties in handling data streams. The current state of dynamic clustering is therefore unsatisfactory as it mainly relies on algorithms based on techniques such as density microclustering and density grid based clustering, which require the tuning of several parameters to work effectively [17].…”

“…In terms of online microclustering, most algorithms in the literature are distance based [17,22,26], whereby new observations are either merged with existing microclusters or form new microclusters based on a distance threshold. The earliest form of distance based clustering strategy was the process of extracting information about a cluster into the form of a Clustering Feature (CF) vector.…”