The proposal of a velocity memoryless clustering swarm

Szabó, Adrienn; Prior, Ana Karina. F.; Castro, Leandro Nunes de

doi:10.1109/cec.2010.5586037

Cited by 15 publications

(27 citation statements)

References 10 publications

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“…Given that the critical contributor to complexity appears to be the updating of the distance matrix, this observation would be consistent with Szabo's report of a small reduction in computation time on mPSC as compared to PSC [9]. As seen in (9) time complexity of PSC position update (O(k)) is small compared to that of distance matrix update (O(i*j*k)).…”

Section: )supporting

confidence: 86%

“…Szabo proposes a modification to the original PSC called the Modified PSC (mPSC) in 2010 [9]. Szabo argues that in the context of man's ability to process knowledge, the use of "velocity memory" in equation (3) is irrelevant.…”

Section: B Modified Particle Swarm Clustering (Mpsc)mentioning

confidence: 99%

“…Szabo has made a contribution to the original PSC by addressing computational time reduction [9]. He provides a comparative table for PSC and mPSC with benchmark datasets.…”

Section: A Observations Regarding Pscmentioning

confidence: 99%

“…Given that there are i particles in a swarm and j elements of k-dimensional data in the set, time complexity for the algorithm O PSC for n iterations can be approximated by (9).…”

Section: )mentioning

confidence: 99%

“…A modified PSC algorithm called Modified PSC (mPSC) was proposed by Szabo in 2010 [9]. With the assumption that the use of the term velocity is not appropriate in the context of social neighborhood, mPSC eliminates the need for velocity and inertia weight during the update procedure.…”

Section: Introductionmentioning

confidence: 99%

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Method for increasing the computation speed of an unsupervised learning approach for data clustering

Yuwono

Moulton

et al. 2012

2012 IEEE Congress on Evolutionary Computation

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Clustering can be especially effective where the data is irregular, noisy and/or not differentiable. A major obstacle for many clustering techniques is that they are computationally expensive, hence limited to smaller data volume and dimension. We propose a lightweight swarm clustering solution called Rapid Centroid Estimation (RCE). Based on our experiments, RCE has significantly quickened optimization time of its predecessors, Particle Swarm Clustering (PSC) and Modified Particle Swarm Clustering (mPSC). Our experimental results show that on benchmark datasets, RCE produces generally better clusters compared to PSC, mPSC, K-means and Fuzzy C-means.Compared with K-means and Fuzzy C-means which produces clusters with 62% and 55% purities on average respectively, thyroid dataset has successfully clustered on average 71% purity in 14.3 seconds.

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Section: )supporting

confidence: 86%

Section: B Modified Particle Swarm Clustering (Mpsc)mentioning

confidence: 99%

“…Szabo has made a contribution to the original PSC by addressing computational time reduction [9]. He provides a comparative table for PSC and mPSC with benchmark datasets.…”

Section: A Observations Regarding Pscmentioning

confidence: 99%

“…Given that there are i particles in a swarm and j elements of k-dimensional data in the set, time complexity for the algorithm O PSC for n iterations can be approximated by (9).…”

Section: )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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