Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications
Recently many researchers are working on cluster analysis as a main tool for exploratory data analysis and data mining. A notable feature is that specialists in different fields of sciences are considering the tool of data clustering to be useful. A major reason is that clustering algorithms and software are flexible in thesensethat diffierent mathematical frameworks are employed in the algorithms and a user can select a suitable method according to his application. Moreover clustering algorithms have diffierent outputs ranging from the old dendrograms of agglomerativeclustering to more recent self-organizingmaps. Thus, a researcher or user can choose an appropriate output suited to his purpose,which is another—exibility of the methods of clustering. An old and still most popular method is the K-means which use K cluster centers. A group of data is gathered around a cluster center and thus forms a cluster. The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reasonwhy we concentrate on fuzzy c-means is that most methodology and application studies infuzzy clusteringusefuzzy c-means,andfuzzy c-meansshouldbe consideredto be a major technique of clustering in general, regardless whether one is interested in fuzzy methods or not. Moreover recent advances in clustering techniques are rapid and we requirea new textbook that includes recent algorithms.We should also note that several books have recently been published but the contents do not include some methods studied herein.
1103818553
Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications
Recently many researchers are working on cluster analysis as a main tool for exploratory data analysis and data mining. A notable feature is that specialists in different fields of sciences are considering the tool of data clustering to be useful. A major reason is that clustering algorithms and software are flexible in thesensethat diffierent mathematical frameworks are employed in the algorithms and a user can select a suitable method according to his application. Moreover clustering algorithms have diffierent outputs ranging from the old dendrograms of agglomerativeclustering to more recent self-organizingmaps. Thus, a researcher or user can choose an appropriate output suited to his purpose,which is another—exibility of the methods of clustering. An old and still most popular method is the K-means which use K cluster centers. A group of data is gathered around a cluster center and thus forms a cluster. The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reasonwhy we concentrate on fuzzy c-means is that most methodology and application studies infuzzy clusteringusefuzzy c-means,andfuzzy c-meansshouldbe consideredto be a major technique of clustering in general, regardless whether one is interested in fuzzy methods or not. Moreover recent advances in clustering techniques are rapid and we requirea new textbook that includes recent algorithms.We should also note that several books have recently been published but the contents do not include some methods studied herein.
169.99
In Stock
5
1
Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications
247
Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications
247Hardcover(2008)
$169.99
169.99
In Stock
Product Details
| ISBN-13: | 9783540787365 |
|---|---|
| Publisher: | Springer Berlin Heidelberg |
| Publication date: | 04/15/2008 |
| Series: | Studies in Fuzziness and Soft Computing , #229 |
| Edition description: | 2008 |
| Pages: | 247 |
| Product dimensions: | 6.14(w) x 9.21(h) x 0.02(d) |
From the B&N Reads Blog