A swarm-inspired projection algorithm

Su, Mu-Chun; Su, Shiyong; Zhao, Yu-Xiang

doi:10.1016/j.patcog.2009.03.020

Cited by 21 publications

(19 citation statements)

References 30 publications

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“…Notably, there is no single "best" criterion for obtaining a clustering because no precise and workable definition of "a cluster" exists [Jain/Dubes, 1988, p. 91]. For the task of dimensionality reduction, the swarm-inspired projection (SIP) method [Su et al, 2009] are discussed later in this section. ACO methods for clustering tasks are referred to as ABC methods (for an overview, see [Kaur/Rohil, 2015]).…”

Section: Swarm Intelligence For Unsupervised Machine Learningmentioning

confidence: 99%

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Projection-Based Clustering through Self-Organization and Swarm Intelligence

Thrun¹

2018

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Section: Swarm Intelligence For Unsupervised Machine Learningmentioning

confidence: 99%

“…22-25]. A third method is known as the swarm-inspired projection (SIP [Su et al, 2009], as briefly mentioned above. SIP is a PSO approach that is loosely related to foraging theory because it is inspired by the foraging behavior of doves.…”

Section: Swarm Intelligence For Unsupervised Machine Learningmentioning

confidence: 99%

Projection-Based Clustering through Self-Organization and Swarm Intelligence

Thrun¹

2018

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“…Dove swarm optimization (DSO) algorithm was recently proposed in Su et al (2009). The basic working principles of DSO are listed as follows (Su et al 2009):…”

Section: Fundamentals Of Dove Swarm Optimization Algorithmmentioning

confidence: 99%

“…In order to determine the optimal number of clusters, Su et al (2009) employed DSO to perform data projection task, i.e., projecting highdimensional data onto a low-dimensional space to facilitate visual inspection of the data. This process allows us to visualize high-dimensional data as a 2-dimensional scatter plot.…”

Section: Performance Of Dsomentioning

confidence: 99%

Emerging Biology-based CI Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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In this chapter, a group of (more specifically 56 in total) emerging biology-based computational intelligence (CI) algorithms are introduced. We first, in Sect. 17.1, describe the organizational structure of this chapter. Then, from Sects. 17.2 to 17.57, each section is dedicated to a specific algorithm which falls within this category, respectively. The fundamentals of each algorithm and their corresponding performances compared with other CI algorithms can be found in each associated section. Finally, the conclusions drawn in Sect. 17.58 closes this chapter.

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“…In particular, it incorporates swarming behaviours observed in flocks of birds, schools of fish, or swarms of bees, colonies of ants, and even human social behavior, from which the intelligence is emerged [1,2,3,4,5]. SI provides a framework to explore distributed problem solving without centralized control or the provision of a global model.…”

Section: Introductionmentioning

confidence: 99%