Journal ArticleDOI
Use of a fuzzy granulation--degranulation criterion for assessing cluster validity
TLDR
A fuzzy granulation-degranulation criterion is proposed to evaluate the goodness of a fuzzy partitioning of the data and this, in turn, is used to determine the appropriate clustering algorithm suitable for a particular data set.About:
This article is published in Fuzzy Sets and Systems.The article was published on 2011-05-01. It has received 23 citations till now. The article focuses on the topics: Fuzzy clustering & Cluster analysis.read more
Citations
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Journal ArticleDOI
Evaluation of clustering algorithms for financial risk analysis using MCDM methods
Gang Kou,Yi Peng,Guoxun Wang +2 more
TL;DR: An MCDM-based approach to rank a selection of popular clustering algorithms in the domain of financial risk analysis and indicates that the repeated-bisection method leads to good 2-way clustering solutions on the selected financial risk data sets.
Journal ArticleDOI
Automatic pattern recognition of ECG signals using entropy-based adaptive dimensionality reduction and clustering
Hong He,Yonghong Tan +1 more
TL;DR: A new entropy-based principal component analysis approach (EPCA) is developed in this paper which can automatically select the best number of principal components of the PCA and a novel fuzzy-entropy based c-means clustering algorithm (FECM) which can adaptively identify the optimal number of clusters based on the ECM.
Proceedings ArticleDOI
A model with Fuzzy Granulation and Deep Belief Networks for exchange rate forecasting
Ren Zhang,Furao Shen,Jinxi Zhao +2 more
TL;DR: A model to forecast the fluctuation range of the exchange rate is presented by combining Fuzzy Granulation with Continuous-valued Deep Belief Networks (CDBN), and the concept of "Stop Loss" is introduced for making the environment of the profit strategy close to the real foreign exchange trade market.
Journal ArticleDOI
Cluster Analysis for mixed data: An application to credit risk evaluation
TL;DR: This paper highlights the relevance of both quantitative and qualitative features of applicants and proposes a new methodology based on mixed data clustering techniques, which may prove particularly useful in the estimation of credit risk.
Journal ArticleDOI
Developing fast predictors for large-scale time series using fuzzy granular support vector machines
Junhu Ruan,Xuping Wang,Yan Shi +2 more
TL;DR: This study aims to develop fast interval predictors for large-scale, nonlinear time series with noisy data using fuzzy granular support vector machines (FGSVMs) using six information granulation methods which can granulate large- scale time series into subseries.
References
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Proceedings Article
A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise
TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
Book
Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Proceedings Article
A density-based algorithm for discovering clusters in large spatial Databases with Noise
TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.
Book
Cluster Analysis
TL;DR: This fourth edition of the highly successful Cluster Analysis represents a thorough revision of the third edition and covers new and developing areas such as classification likelihood and neural networks for clustering.