Weight-oriented possibilistic MKFC algorithm

TANG Yi-ming; SONG Xiao-cheng; REN Fu-ji; FENG Gang-yong

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Weight-oriented possibilistic MKFC algorithm

更新时间：2026-09-04

- Weight-oriented possibilistic MKFC algorithm
- Vol. 37, Issue 12, Pages: 6-11(2020)
- 作者机构：
  
  合肥工业大学计算机与信息学院,安徽,合肥,230009
- 作者简介：
- 基金信息：
- DOI：
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- Published：2020
- 稿件说明：
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唐益明, 宋小成, 任福继, 丰刚永. 面向权重的可能性MKFC算法[J]. 微电子学与计算机, 2020, 37(12): 6-11.

TANG Yi-ming, SONG Xiao-cheng, REN Fu-ji, et al. Weight-oriented possibilistic MKFC algorithm[J]. 2020, 37(12): 6-11.
唐益明, 宋小成, 任福继, 丰刚永. 面向权重的可能性MKFC算法[J]. 微电子学与计算机, 2020, 37(12): 6-11. DOI：

TANG Yi-ming, SONG Xiao-cheng, REN Fu-ji, et al. Weight-oriented possibilistic MKFC algorithm[J]. 2020, 37(12): 6-11. DOI：

摘要

基于多核的模糊聚类(MKFC)是当前聚类领域的最新热点，但是其很难通过人工方式确定在组合中核函数权重大小，并更好地调整所使用的不同内核函数的权重比.为了解决以上问题，设计了自动称量MKFC算法的提案.首先，给出了该算法目标函数的核心公式及其内在思想；其次，给出了Mercer核函数及多核距离的计算方法；然后给出了核函数权重的自动计算模式，由此形成了所提算法的流程；最后，在8个UCI数据库中进行测试，凭借与5种算法的分析对比，发现所提算法是较为理想的聚类方法.

Abstract

Multi-kernel fuzzy clustering (MKFC) is a hot topic in the field of clustering.However

it is difficult to manually determine the weight of kernel function in the combination

and difficult to allocate the weight of kernel function ideally. Focusing on such problem

a weight-oriented possibilistic MKFC algorithm was put forward

which is based on multi-kernel mechanism

possibilistic clustering and automatic weight calculation.Firstly

the core formula of the objective function and its inner idea were given.Secondly

the calculation methods ofthe Mercer kernel function and multi-kernel distance were given.Then the automatic calculation mode of kernel function weight was given and the flow of the proposed algorithm was formed.Finally

the proposed algorithm is tested in 8 UCI databases

and compared with the 5 algorithms.It is found that the proposed algorithm is an ideal clustering method.

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Keywords

references

GONEN M, ALPAYDINE. Localized multiple kernel learning[C]. In: Proc. 25th Annu. Int. Conf. Mach. Learning, 2008: 352-359.

YANG M S, TSAI H S.A Gaussian kernel-based fuzzy c -means algorithm with a spatial bias correction[J]. Pattern Recognition Letters, 2008, 29(12):1713-1725. DOI: 1713

LANCKRIETGR, DEBT, CRISTIANINI N, et al. A statistical framework for genomic data fusion[J]. Bioinformatics, 2004, 20(16):2626-2635. DOI: 2626

ZHANG J S, LEUNGYW. Improved Possibilistic C-Means Clustering Algorithms[J]. IEEE Transactions on Fuzzy Systems, 2004, 12(2):209-217. DOI: 209

KOUTROUMBASKD, XENAKISD, RONTOGIANNIS A A. On the Convergence of the Sparse Possibilistic C-Means Algorith[J]. IEEE Transactions on Fuzzy Systems, 2018, 26(1): 324-337. DOI: 324

TZORTZISG, LIKASA. The global kernel k-means algorithm for clustering in feature space. IEEE Transactions on Neural Networks[J]. IEEE Transactions on Neural Networks, 2009, 20(7):1181-1194. DOI: 1181

CHEN S C, ZHANG D Q. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J]. IEEE Transactions on System Man and Cybernetics - Part B, 2004, 34(4):1907-1916. DOI: 1907

HUANG H C, CHUANG Y Y. Multiple Kernel Fuzzy Clustering[J]. IEEE Transactions on Fuzzy Systems, 2012, 20(1):120-134. DOI: 120

BACHFR, LANCKRIETRG, JORDAN I. Multiple kernel learning, conic duality, and the SMO algorithm[C]. International Conference of DBLP. 2008, 6-13.

KELLERJM, KRISHNAPURAMR. A possibilistic approach to clustering[J]. IEEE Transaction on Fuzzy Systems, 1993, 1(2):98-110. DOI: 98

PALN R, PAL K, JAMES M, et al. Possibilistic Fuzzy c-Means Clustering Algorithm[J]. IEEE Transaction on Fuzzy Systems, 2005, 13(4):107-116. DOI: 107

ZHAO B, KWOKJ, ZHANG C. Multiple kernel clustering[C]. In: Proc. 9th SIAM Int. Conf. Data Mining, 2009: 638-649.

TANG Y M, PEDRYCZW. On the α(u, v)-symmetric implicational method for R- and (S, N)-implications[J]. International Journal of Approximate Reasoning, 2018, 92(1): 212-231. DOI: 212

TANG Y M, HU X H, PEDRYCZW, et al.Possibilistic fuzzy clustering with high-density viewpoint[J]. Neurocomputing, 2019, 329(1): 407-423. DOI: 407

NGUYENDD, NGOLT, PHAM L T, et al. Towards hybrid clustering approach to data classification: Multiple kernels based interval-valued fuzzy C-means algorithms[J]. Fuzzy Sets and Systems, 2015, 27(9):17-39. DOI: 17

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