Concept-evolution detection in non-stationary data streams: a fuzzy clustering approach

Zaremoodi, Poorya; Siahroudi, Sajjad Kamali; Beigy, Hamid

doi:10.1007/s10115-018-1266-y

Cited by 17 publications

(2 citation statements)

References 24 publications

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“…end if 13: else if p ≥ 0.5 then 14: Update the position of current whale x i with Equation (2) 15: end if 16: end for 17: Calculate new fitness values 18: Update X best 19: t = t + 1 20: end while 21: Return x best With reference to Algorithm 1, the initial swarm is generated by randomly sampling solutions in the search; the best solution is kept up to date by replacing it only when an improvement on the fitness value occurs; the optimisation process lasts for a prefixed number of iterations, here indicated with max budget; the probability of using the shrinking encircling rather than the spiral updating mechanism was fixed at 0.5.…”

Section: Algorithm 1 Woa Pseudocodementioning

confidence: 99%

“…There are two fundamentals aspects to take into consideration in data stream clustering, namely concept drift and concept evolution. The first aspect refers to the phenomenon when the data in the stream undergo changes in the statistical properties of the clusters with respect to the time [19,20] while the second to the event when there is an unseen novel cluster appearing in the stream [5,21].…”

Section: Introductionmentioning

confidence: 99%

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A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

et al. 2019

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This article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses "microclusters" and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream's robustness to noise and resiliency to parameter changes.

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Section: Algorithm 1 Woa Pseudocodementioning

confidence: 99%