Online fuzzy c means

Hore, Prodip; Hall, Lawrence O.; Goldgof, Dmitry; Cheng, Weijian

doi:10.1109/nafips.2008.4531233

Cited by 56 publications

(28 citation statements)

References 24 publications

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“…In our experiments, we used the cluster centers from the previous PDA as an initialization. While this matches the original implementation of the algorithm [27], a poor initialization will be produced by PDAs largely consisting of just one class. Another feature of OFCM is that the dataset is not assumed to be in random order.…”

Section: Fuzzy C-means (Fcm) Based Algorithmssupporting

confidence: 55%

Accelerating Fuzzy-C Means Using an Estimated Subsample Size

Parker

Hall

2014

IEEE Trans. Fuzzy Syst.

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Many algorithms designed to accelerate the Fuzzy c-Means (FCM) clustering algorithm randomly sample the data. Typically, no statistical method is used to estimate the subsample size, despite the impact subsample sizes have on speed and quality. This paper introduces two new accelerated algorithms, GOFCM and MSERFCM, that use a statistical method to estimate the subsample size. GOFCM, a variant of SPFCM, also leverages progressive sampling. MSERFCM, a variant of rseFCM, gains a speedup from improved initialization. A general, novel stopping criterion for accelerated clustering is introduced. The new algorithms are compared to FCM and four accelerated variants of FCM. GOFCM's speedup was 4-47 times that of FCM and faster than SPFCM on each of the six datasets used in experiments. For five of the datasets, partitions were within 1% of those of FCM. MSERFCM's speedup was 5-26 times that of FCM and produced partitions within 3% of those of FCM on all datasets. A unique dataset, consisting of plankton images, exposed the strengths and weaknesses of many of the algorithms tested. It is shown that the new stopping criterion is effective in speeding up algorithms such as SPFCM and the final partitions are very close to those of FCM.

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Section: Fuzzy C-means (Fcm) Based Algorithmssupporting

confidence: 55%

Accelerating Fuzzy-C Means Using an Estimated Subsample Size

Parker

Hall

2014

IEEE Trans. Fuzzy Syst.

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“…On the other hand, several incremental fuzzy approaches were designed to process large data clustering in a chunk-by-chunk way. Typical examples include Single-pass Fuzzy C Means (SPFCM) [28], Online Fuzzy C Means (OFCM) [29] and Incremental Multiple Medoids-based Fuzzy Clustering (IMMFC) [37].…”

Section: Related Workmentioning

confidence: 99%

Incremental enhanced α-expansion move for large data: a probability regularization perspective

Wang

2016

Int. J. Mach. Learn. & Cyber.

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To deal with large data clustering tasks, an incremental version of exemplar-based clustering algorithm is proposed in this paper. The novel clustering algorithm, called Incremental Enhanced a-Expansion Move (IEEM), processes large data chunk by chunk. The work here includes two aspects. First, in terms of the maximum a posteriori principle, a unified target function is developed to unify two typical exemplar-based clustering algorithms, namely Affinity Propagation (AP) and Enhanced aExpansion Move (EEM). Secondly, with the proposed target function, the probability based regularization term is proposed and accordingly the proposed target function is extended to make IEEM have the ability to improve clustering performance of the entire dataset by leveraging the clustering result of previous chunks. Another outstanding characteristic of IEEM is that only by modifying the definitions of several variables used in EEM, the minimization procedure of EEM and its theoretical spirit can be easily kept in IEEM, and hence no more efforts are needed to develop a new optimization algorithm for IEEM. In contrast to AP, EEM and the existing incremental clustering algorithm IMMFC, our experimental results of synthetic and real-world datasets indicate the effectiveness of IEEM.

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“…This algorithm divides the data set into chunks and clusters each chunk in sequence using the Weighted Fuzzy C-Means algorithm (WFCM) [4]. The weighted FCM -Adaptive Cluster [15] and Online Fuzzy C-Means [11] are examples of algorithms based on this approach. A survey on fuzzy methods for data streams clustering can be found in [1].…”

Section: Related Workmentioning

confidence: 99%

Merging Clusters in Summary Structures for Data Stream Mining based on Fuzzy Similarity Measures

Schick

Lopes

Camargo

2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Fuzzy Clustering is one of the mining techniques that have been used to extract information from Data Streams.The d-FuzzStream algorithm is a fuzzy version of the Online-Offline Framework, which consists of two steps: an online step, where a summary structure formed by fuzzy microclusters is built and an offline step, where the micro-clusters are clustered in batch mode. The quality of the data summary depends on the criteria used to decide whether an example starts a new micro-cluster or is absorbed by the existing ones; and whether two microclusters became similar enough to be merged. In d-FuzzStream algorithm such decisions are based on concepts of fuzzy dispersion and a distance-based fuzzy clusters similarity. In this paper we investigate the behavior of different fuzzy similarity measures on the decision of merging two fuzzy micro-clusters during the online step. Experiments were run using five synthetic data sets and four fuzzy similarity measures. The results obtained are analyzed and discussed through informative and purity measures.

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Online fuzzy c means

Cited by 56 publications

References 24 publications

Accelerating Fuzzy-C Means Using an Estimated Subsample Size

Accelerating Fuzzy-C Means Using an Estimated Subsample Size

Incremental enhanced α-expansion move for large data: a probability regularization perspective

Merging Clusters in Summary Structures for Data Stream Mining based on Fuzzy Similarity Measures

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