Fast global kernel fuzzy c-means clustering algorithm for consonant/vowel segmentation of speech signal

Zang, Xian; Vista, Felipe P.; Chong, Kil To

doi:10.1631/jzus.c1300320

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…A fuzzy clustering version is also available (Zang et al. 2014 ). All of these are incremental approaches that start from one cluster and at each step a new cluster is deterministically added to the solution according to an appropriate criterion.…”

Section: Introductionmentioning

confidence: 99%

The global Minmax k-means algorithm

Wang

Bai

2016

SpringerPlus

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The global k-means algorithm is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure from suitable initial positions, and employs k-means to minimize the sum of the intra-cluster variances. However the global k-means algorithm sometimes results singleton clusters and the initial positions sometimes are bad, after a bad initialization, poor local optimal can be easily obtained by k-means algorithm. In this paper, we modified the global k-means algorithm to eliminate the singleton clusters at first, and then we apply MinMax k-means clustering error method to global k-means algorithm to overcome the effect of bad initialization, proposed the global Minmax k-means algorithm. The proposed clustering method is tested on some popular data sets and compared to the k-means algorithm, the global k-means algorithm and the MinMax k-means algorithm. The experiment results show our proposed algorithm outperforms other algorithms mentioned in the paper.

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Section: Introductionmentioning

confidence: 99%

The global Minmax k-means algorithm

Wang

Bai

2016

SpringerPlus

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“…Tzortzis and Likas extended the algorithm to kernel space [ 8 , 9 ]. Zang et al developed a fuzzy c -means clustering algorithm and applied such algorithm to the investigation of speech signal [ 10 ]. An alternative approach to eliminate the influence of initial starting conditions is to use the multi-restarting k -means algorithm [ 11 – 14 ].…”

Section: Introductionmentioning

confidence: 99%

A Modified MinMaxk-Means Algorithm Based on PSO

Wang

Bai

2016

Computational Intelligence and Neuroscience

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The MinMax k-means algorithm is widely used to tackle the effect of bad initialization by minimizing the maximum intraclustering errors. Two parameters, including the exponent parameter and memory parameter, are involved in the executive process. Since different parameters have different clustering errors, it is crucial to choose appropriate parameters. In the original algorithm, a practical framework is given. Such framework extends the MinMax k-means to automatically adapt the exponent parameter to the data set. It has been believed that if the maximum exponent parameter has been set, then the programme can reach the lowest intraclustering errors. However, our experiments show that this is not always correct. In this paper, we modified the MinMax k-means algorithm by PSO to determine the proper values of parameters which can subject the algorithm to attain the lowest clustering errors. The proposed clustering method is tested on some favorite data sets in several different initial situations and is compared to the k-means algorithm and the original MinMax k-means algorithm. The experimental results indicate that our proposed algorithm can reach the lowest clustering errors automatically.

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“…Clustering and mixture model approaches are typical of monitoring methods used for multimode processes (Teppola et al, 1999;Xu et al, 2011;Feital et al, 2013;Ge et al, 2013;Song et al, 2014). The aim of data clustering is to group observations into multiple clusters that represent the underlying data patterns (Zang et al, 2014). Due to a natural connection with multimode modeling, a clustering approach is employed to partition multimode process data into multiple linear spaces which are assumed to follow Gaussian distributions, and linear MSPC methods are then applied in each of these linear subspaces.…”

Section: Introductionmentioning

confidence: 99%

A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Ren

Yin

Zhao

et al. 2015

Frontiers Inf Technol Electronic Eng

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A local discriminant regularized soft k-means (LDRSKM) method with Bayesian inference is proposed for multimode process monitoring. LDRSKM extends the regularized soft k-means algorithm by exploiting the local and non-local geometric information of the data and generalized linear discriminant analysis to provide a better and more meaningful data partition. LDRSKM can perform clustering and subspace selection simultaneously, enhancing the separability of data residing in different clusters. With the data partition obtained, kernel support vector data description (KSVDD) is used to establish the monitoring statistics and control limits. Two Bayesian inference based global fault detection indicators are then developed using the local monitoring results associated with principal and residual subspaces. Based on clustering analysis, Bayesian inference and manifold learning methods, the within and cross-mode correlations, and local geometric information can be exploited to enhance monitoring performances for nonlinear and non-Gaussian processes. The effectiveness and efficiency of the proposed method are evaluated using the Tennessee Eastman benchmark process.

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Fast global kernel fuzzy c-means clustering algorithm for consonant/vowel segmentation of speech signal

Cited by 7 publications

References 25 publications

The global Minmax k-means algorithm

The global Minmax k-means algorithm

A Modified MinMaxk-Means Algorithm Based on PSO

A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

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