scispace - formally typeset
Topic

Fuzzy clustering

About: Fuzzy clustering is a(n) research topic. Over the lifetime, 23230 publication(s) have been published within this topic receiving 601269 citation(s).

...read more

Papers
More filters

Book
31 Jul 1981-
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.

...read more

Abstract: New updated! The latest book from a very famous author finally comes out. Book of pattern recognition with fuzzy objective function algorithms, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.

...read more

15,070 citations


Book
Anil K. Jain1, Richard C. Dubes1Institutions (1)
01 Jan 1988-

8,580 citations


Proceedings Article
Andrew Y. Ng1, Michael I. Jordan1, Yair Weiss2Institutions (2)
03 Jan 2001-
TL;DR: A simple spectral clustering algorithm that can be implemented using a few lines of Matlab is presented, and tools from matrix perturbation theory are used to analyze the algorithm, and give conditions under which it can be expected to do well.

...read more

Abstract: Despite many empirical successes of spectral clustering methods— algorithms that cluster points using eigenvectors of matrices derived from the data—there are several unresolved issues. First. there are a wide variety of algorithms that use the eigenvectors in slightly different ways. Second, many of these algorithms have no proof that they will actually compute a reasonable clustering. In this paper, we present a simple spectral clustering algorithm that can be implemented using a few lines of Matlab. Using tools from matrix perturbation theory, we analyze the algorithm, and give conditions under which it can be expected to do well. We also show surprisingly good experimental results on a number of challenging clustering problems.

...read more

8,315 citations


Journal ArticleDOI
Ulrike von Luxburg1Institutions (1)
Abstract: In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at all and what it really does. The goal of this tutorial is to give some intuition on those questions. We describe different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Advantages and disadvantages of the different spectral clustering algorithms are discussed.

...read more

8,197 citations


Journal ArticleDOI
Anil K. Jain1Institutions (1)
01 Jun 2010-
TL;DR: A brief overview of clustering is provided, well known clustering methods are summarized, the major challenges and key issues in designing clustering algorithms are discussed, and some of the emerging and useful research directions are pointed out.

...read more

Abstract: Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into a system of ranked taxa: domain, kingdom, phylum, class, etc. Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is to find structure in data and is therefore exploratory in nature. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been published since then, K-means is still widely used. This speaks to the difficulty in designing a general purpose clustering algorithm and the ill-posed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semi-supervised clustering, ensemble clustering, simultaneous feature selection during data clustering, and large scale data clustering.

...read more

5,786 citations


Network Information
Related Topics (5)
Correlation clustering

19.3K papers, 602.5K citations

95% related
Canopy clustering algorithm

12K papers, 339.4K citations

93% related
Association rule learning

15.1K papers, 362K citations

93% related
CURE data clustering algorithm

13.7K papers, 461.2K citations

92% related
Support vector machine

73.6K papers, 1.7M citations

92% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202217
2021451
2020460
2019587
2018569
20171,087

Top Attributes

Show by:

Topic's top 5 most impactful authors

Witold Pedrycz

193 papers, 9K citations

Katsuhiro Honda

130 papers, 1.1K citations

Sadaaki Miyamoto

97 papers, 1.1K citations

Hidetomo Ichihashi

72 papers, 774 citations

Sanghamitra Bandyopadhyay

64 papers, 6.3K citations