3,440 research outputs found
Autonomous clustering using rough set theory
This paper proposes a clustering technique that minimises the need for subjective
human intervention and is based on elements of rough set theory. The proposed algorithm is
unified in its approach to clustering and makes use of both local and global data properties to
obtain clustering solutions. It handles single-type and mixed attribute data sets with ease and
results from three data sets of single and mixed attribute types are used to illustrate the
technique and establish its efficiency
Accelerated Fuzzy C-Means Clustering Based on New Affinity Filtering and Membership Scaling
Fuzzy C-Means (FCM) is a widely used clustering method. However, FCM and its
many accelerated variants have low efficiency in the mid-to-late stage of the
clustering process. In this stage, all samples are involved in the update of
their non-affinity centers, and the fuzzy membership grades of the most of
samples, whose assignment is unchanged, are still updated by calculating the
samples-centers distances. All those lead to the algorithms converging slowly.
In this paper, a new affinity filtering technique is developed to recognize a
complete set of the non-affinity centers for each sample with low computations.
Then, a new membership scaling technique is suggested to set the membership
grades between each sample and its non-affinity centers to 0 and maintain the
fuzzy membership grades for others. By integrating those two techniques, FCM
based on new affinity filtering and membership scaling (AMFCM) is proposed to
accelerate the whole convergence process of FCM. Many experimental results
performed on synthetic and real-world data sets have shown the feasibility and
efficiency of the proposed algorithm. Compared with the state-of-the-art
algorithms, AMFCM is significantly faster and more effective. For example,
AMFCM reduces the number of the iteration of FCM by 80% on average
Fuzzy-Rough Set based Semi-Supervised Learning
AbstractâMuch work has been carried out in the area of fuzzy-rough sets for supervised learning. However, very little has been accomplished for the unsupervised or semi-supervised tasks. For many real-word applications, it is often expensive, time-consuming and difficult to obtain labels for all data objects. This often results in large quantities of data which may only have very few labelled data objects. This paper proposes a novel fuzzy-rough based semi-supervised self-learning or self-training approach for the assignment of labels to unlabelled data. Unlike other semi-supervised approaches, the proposed technique requires no subjective thresholding or domain information. An experimental evaluation is performed on artificial data and also applied to a real-world mammographic risk assessment problem with encouraging results. Index TermsâRough sets, fuzzy sets, mammographic analysis, semi-supervised learning I
Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A review
Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets theory. In 1965, L.A. Zadeh had published âFuzzy Setsâ [335]. After only one year, the first effects of this seminal paper began to emerge, with the pioneering paper on clustering by Bellman, Kalaba, Zadeh [33], in which they proposed a prototypal of clustering algorithm based on the fuzzy sets theory
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