Journal ArticleDOI
Validity index for crisp and fuzzy clusters
TLDR
A cluster validity index and its fuzzification is described, which can provide a measure of goodness of clustering on different partitions of a data set, and results demonstrating the superiority of the PBM-index in appropriately determining the number of clusters are provided.About:
This article is published in Pattern Recognition.The article was published on 2004-03-01. It has received 710 citations till now. The article focuses on the topics: Fuzzy clustering & Correlation clustering.read more
Citations
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Book ChapterDOI
Modern approaches of signal processing for bidirectional neural interfaces
TL;DR: In this article, the authors introduce modern approaches and future applications of advanced signal processing techniques for neural invasive electrodes for bidirectional neural interfaces, and propose a reliable closed-loop system that would bidirectionally interface with the central and peripheral nervous system for the optimal control of neuroprosthetic devices and neurorehabilitative procedures.
Book ChapterDOI
Residential Electricity Consumption Pattern Mining Based on Fuzzy Clustering
Kaile Zhou,Lulu Wen +1 more
Journal ArticleDOI
Immune Clonal Spectral Clustering for PolSAR Land Cover Classification
TL;DR: The proposed method combines the complementary advantage of spectral clustering and immune clonal clustering to reduce dimension in the polarimetric feature space and is more stable and efficient compared with the other methods.
Proceedings Article
A cluster validity index based on frequent pattern
TL;DR: A CV index for fuzzy-clustering algorithm, such as the fuzzy c-means (FCM) or its derivatives, given a fuzzy partition is proposed, which uses global information and is based on more logical reasoning than geometrical features.
Posted ContentDOI
Omada: Robust clustering of transcriptomes through multiple testing
Sokratis Kariotis,Tan Pei Fang,Haiping Lu,Christopher J. Rhodes,Martin Wilkins,Allan Lawrie,Dennis Wang +6 more
TL;DR: Omada as discussed by the authors is a suite of tools aiming to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning based functions, which are tested with five datasets characterised by different expression signal strengths to capture a wide spectrum of RNA expression datasets.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Applied Multivariate Statistical Analysis
R. A. Johnson,Dean W. Wichern +1 more
TL;DR: In this article, the authors present an overview of the basic concepts of multivariate analysis, including matrix algebra and random vectors, as well as a strategy for analyzing multivariate models.
Journal ArticleDOI
Applied Multivariate Statistical Analysis.
TL;DR: In this article, the authors present an overview of the basic concepts of multivariate analysis, including matrix algebra and random vectors, as well as a strategy for analyzing multivariate models.