A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A Novel Approach to Noise Clustering for Outlier Detection
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on soft computing for information mining
Fast mining of distance-based outliers in high-dimensional datasets
Data Mining and Knowledge Discovery
Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm
Computational Statistics & Data Analysis
Detecting outlier samples in multivariate time series dataset
Knowledge-Based Systems
A Weighting Fuzzy Clustering Algorithm Based on Euclidean Distance
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Data analysis with fuzzy clustering methods
Computational Statistics & Data Analysis
Generalized weighted conditional fuzzy clustering
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
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In this paper, the intelligent techniques are utilized to enhance the quality control precision in the steel strip cold rolling production. Firstly, a new control scheme is proposed, establishing the classifier of the steel strip flatness signal is the basis of the scheme. The fuzzy clustering method is used to establish the classifier. Getting the high quality clustering prototypes is one of the key tasks. Secondly, a kind of new fuzzy clustering model, generalized fuzzy C-means clustering (GeFCM) model, is proposed and used as the mining tools in the real applications. The results, under the comparisons with the results obtained by the basic fuzzy clustering model, show the GeFCM is robust and efficient and it not only can get much better clustering prototypes, which are used as the classifier, but also can easily and effectively mine the outliers. It is very helpful in the steel strip flatness quality control system in one real cold rolling line. Finally, it is pointed out that the new model's efficiency is mainly due to the introduction of a set of adaptive degrees wj (j=1...n, and n is the number the data objects) and an adaptive exponent p which jointly affect the clustering operations. In nature, the proposed GeFCM model is the generalized version of the existing fuzzy clustering models.