A Fault Diagnosis Method Based on Semi - Supervised Fuzzy C - Means Cluster Analysis
نویسندگان
چکیده
منابع مشابه
Semi-supervised Kernel-Based Fuzzy C-Means
This paper presents a semi-supervised kernel-based fuzzy c-means algorithm called S2KFCM by introducing semi-supervised learning technique and the kernel method simultaneously into conventional fuzzy clustering algorithm. Through using labeled and unlabeled data together, S2KFCM can be applied to both clustering and classification tasks. However, only the latter is concerned in this paper. Expe...
متن کاملText Categorization using the Semi-Supervised Fuzzy c-Means Algorithm
Text Categorization (TC) is the automated assignment of text documents to predefined categories based on document contents. For the past few years, TC has become very important essentially in the Information Retrieval area, where information needs have tremendously increased with the rapid growth of textual information sources such as the Internet. In this paper, we compare , for text categoriz...
متن کاملImprove Semi-Supervised Fuzzy C-means Clustering Based On Feature Weighting
Semi-supervised learning is somewhere between unsupervised and supervised learning. In fact, most semi-supervised learning strategies are based on extending either unsupervised or supervised learning to include additional information typical of the other learning paradigm. Constraint fuzzy c-means a novel semi-supervised fuzzy c-means algorithm proposed by Li et al [1]. Constraint FCM like FCM ...
متن کاملParallel Fuzzy c-Means Cluster Analysis
This work presents an implementation of a parallel Fuzzy c-means cluster analysis tool, which implements both aspects of cluster investigation: the calculation of clusters’ centers with the degrees of membership of records to clusters, and the determination of the optimal number of clusters for a given dataset using the PBM index. Topics of Interest: Unsupervised Classification, Fuzzy c-Means, ...
متن کاملA Fault Diagnosis Method for Automaton based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition
In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal on Cybernetics & Informatics
سال: 2015
ISSN: 2320-8430,2277-548X
DOI: 10.5121/ijci.2015.4227