Iterative shrinking method for clustering problems

Fränti, Pasi; Virmajoki, Olli

doi:10.1016/j.patcog.2005.09.012

Cited by 287 publications

(110 citation statements)

References 28 publications

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“…Regarding clustering quality, the results are consistent with the comparisons made with image data [24]. We expect the results to generalize to other variations of spectral features as well, and to some extent, to other pattern recognition applications.…”

Section: Discussionsupporting

confidence: 84%

“…Usually K-means [52] and expectation-maximization (EM) [5,57] methods have been used, although several better clustering methods exist [24]. This raises the questions of which clustering algorithm should be chosen, and whether the choice between VQ or GMM model matters.…”

Section: Review Of Clustering Methods In Speaker Recognitionmentioning

confidence: 99%

“…Even though several literature surveys exist [36,37,60,77], only a few extensive comparisons are available in image processing [24] and text retrieval [76] but none in speaker recognition. In clustering research, new methods are usually compared in terms of clustering quality.…”

Section: Relevance Of Clustering In a Large-scale Pattern Recognitionmentioning

confidence: 99%

“…Thus, all methods for data sets consisting of thousands or millions of training vectors are based on different heuristics; several hundreds of methods have been proposed in literature [36]. For the comparisons in this paper, we include algorithms that, according to our experience [24], consistently provide high quality clustering, and algorithms that are popular due to their simplicity or for other reasons. We include two hierarchical algorithms (PNN, SPLIT) and six iterative algorithms (K-means, SOM, RS, SM, FCM, GA).…”

Section: Clustering Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

Comparison of clustering methods: A case study of text-independent speaker modeling

Kinnunen¹,

Sidoroff²,

Tuononen³

et al. 2011

Pattern Recognition Letters

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Clustering is needed in various applications such as biometric person authentication, speech coding and recognition, image compression and information retrieval. Hundreds of clustering methods have been proposed for the task in various fields but, surprisingly, there are few extensive studies actually comparing them. An important question is how much the choice of a clustering method matters for the final pattern recognition application. Our goal is to provide a thorough experimental comparison of clustering methods for text-independent speaker verification. We consider parametric Gaussian mixture model (GMM) and non-parametric vector quantization (VQ) model using the best known clustering algorithms including iterative (K-means, random swap, expectation-maximization), hierarchical (pairwise nearest neighbor, split, split-and-merge), evolutionary (genetic algorithm), neural (self-organizing map) and fuzzy (fuzzy C-means) approaches. We study recognition accuracy, processing time, clustering validity, and correlation of clustering quality and recognition accuracy. Experiments from these complementary observations indicate clustering is not a critical task in speaker recognition and the choice of the algorithm should be based on computational complexity and simplicity of the implementation. This is mainly because of three reasons: the data is not clustered, large models are used and only the best algorithms are considered. For low-order models, choice of the algorithm, however, can have a significant effect. Index Terms

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Section: Discussionsupporting

confidence: 84%

Section: Review Of Clustering Methods In Speaker Recognitionmentioning

confidence: 99%

Section: Relevance Of Clustering In a Large-scale Pattern Recognitionmentioning

confidence: 99%

Section: Clustering Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of clustering methods: A case study of text-independent speaker modeling

Kinnunen¹,

Sidoroff²,

Tuononen³

et al. 2011

Pattern Recognition Letters

Self Cite

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show abstract

“…For the experiments we used a set of benchmark datasets -based on real-world examples taken from the UCI Machine Learning Repository [18]. In the same time a set of standard synthetic clustering benchmark instances known as S-sets was used [19]. Table I provides the description of the datasets used in the numerical experiments.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Data Clustering with Grasshopper Optimization Algorithm

Łukasik¹,

Kowalski²,

Charytanowicz³

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-Dividing a dataset into disjoint groups of homogeneous structure, known as data clustering, constitutes an important problem of data analysis. It can be solved with broad range of methods employing statistical approaches or heuristic procedures. The latter often include mechanisms known from nature as they are known to serve as useful components of effective optimizers. The paper investigates the possibility of using novel nature-inspired technique -Grasshopper Optimization Algorithm (GOA) -to generate accurate data clusterings. As a quality measure of produced solutions internal clustering validation measure of Calinski-Harabasz index is being employed. This paper provides description of proposed algorithm along with its experimental evaluation for a set of benchmark instances. Over a course of our study it was established that clustering based on GOA is characterized by high accuracy -when compared with standard K-means procedure.

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Density peaks clustering based on circular partition and grid similarity

Zhao

Tang

Fan

et al. 2019

Concurrency and Computation

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Summary In density peaks clustering, its complexity for computing local density and relative distance of samples raises a scalability issue for processing large datasets. To address the issue, density peaks clustering based on circular partition and grid similarity has been proposed. The algorithm partitions the data space into circular grids, with each grid treated as a sample, for determining the number of clusters and searching for the density peaks; then, a new grid similarity is calculated to effectively assign unallocated grids. The proposed circular partition method effectively reduces the number of samples and the computational complexity. Extensive experiments have been conducted on several datasets with arbitrary shapes and scales, and the proposed method outperforms other density peaks clustering variants in terms of clustering accuracy and efficiency.

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Iterative shrinking method for clustering problems

Cited by 287 publications

References 28 publications

Comparison of clustering methods: A case study of text-independent speaker modeling

Comparison of clustering methods: A case study of text-independent speaker modeling

Data Clustering with Grasshopper Optimization Algorithm

Density peaks clustering based on circular partition and grid similarity

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