نتایج جستجو برای: fuzzy c means clustering algorithms

تعداد نتایج: 1808735  

Journal: :International Journal of Electronics Signals and Systems 2011

Journal: :International Journal of Computer Applications 2016

Journal: :Journal of Biomedical Informatics 2009

Journal: :IEEE Transactions on Fuzzy Systems 2013

Journal: :Int. J. Machine Learning & Cybernetics 2015
Simone A. Ludwig

The management and analysis of big data has been identified as one of the most important emerging needs in recent years. This is because of the sheer volume and increasing complexity of data being created or collected. Current clustering algorithms can not handle big data, and therefore, scalable solutions are necessary. Since fuzzy clustering algorithms have shown to outperform hard clustering...

2015
P. Sampath

Web page prediction is a technique of web usage mining used to predict the next set of web pages that a user may visit based on the knowledge of previously visited web pages. The World Wide Web (WWW) is a popular and interactive medium for publishing the information. While browsing the web, users are visiting many unwanted pages instead of targeted page. The web usage mining techniques are used...

2009
Mohammad Hossein Fazel Zarandi Milad Avazbeigi I. Burhan Türksen

Fuzzy C-Means (FCM) and hard clustering are the most common tools for data partitioning. However, the presence of noisy observations in the data may cause generation of completely unreliable partitions from these clustering algorithms. Also, application of the Euclidean distance in FCM only produces spherical clusters. In this paper, a new noise-rejection clustering algorithm based on Mahalanob...

2009
Hsiang-Chuan Liu Bai-Cheng Jeng Jeng-Ming Yih Yen-Kuei Yu

Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, the former needs added constraint of fuzzy covariance matrix, the later can only be used for the d...

2009
Yukihiro Hamasuna Yasunori Endo Sadaaki Miyamoto

We have proposed tolerant fuzzy c-means clustering (TFCM) from the viewpoint of handling data more flexibly. This paper presents a new type of tolerant fuzzy c-means clustering with L1-regularization. L1-regularization is wellknown as the most successful techniques to induce sparseness. The proposed algorithm is different from the viewpoint of the sparseness for tolerance vector. In the origina...

Journal: :middle east journal of cancer 0
amirehsan lashkari department of bio-medical engineering, institute of electrical engineering & information technology, iranian research organization for science and technology (irost), tehran, iran

background: in this paper we compare a highly accurate supervised to an unsupervised technique that uses breast thermal images with the aim of assisting physicians in early detection of breast cancer. methods: first, we segmented the images and determined the region of interest. then, 23 features that included statistical, morphological, frequency domain, histogram and gray-level co-occurrence ...

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