کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
496096 862850 2013 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A multivariate fuzzy c-means method
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
A multivariate fuzzy c-means method
چکیده انگلیسی

Fuzzy c-means (FCMs) is an important and popular unsupervised partitioning algorithm used in several application domains such as pattern recognition, machine learning and data mining. Although the FCM has shown good performance in detecting clusters, the membership values for each individual computed to each of the clusters cannot indicate how well the individuals are classified. In this paper, a new approach to handle the memberships based on the inherent information in each feature is presented. The algorithm produces a membership matrix for each individual, the membership values are between zero and one and measure the similarity of this individual to the center of each cluster according to each feature. These values can change at each iteration of the algorithm and they are different from one feature to another and from one cluster to another in order to increase the performance of the fuzzy c-means clustering algorithm. To obtain a fuzzy partition by class of the input data set, a way to compute the class membership values is also proposed in this work. Experiments with synthetic and real data sets show that the proposed approach produces good quality of clustering.

Figure optionsDownload as PowerPoint slideHighlights
► Fuzzy c-means algorithm has shown good performance in detecting clusters.
► In this paper the algorithm produces a membership matrix for each individual.
► The membership values are different from one feature to another and from one cluster to another.
► The performance is improved.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 13, Issue 4, April 2013, Pages 1592–1607
نویسندگان
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