Empirical Comparisons of Clustering Algorithms using Silhouette Information
نویسندگان
چکیده
منابع مشابه
Comparisons Between Data Clustering Algorithms
Clustering is a division of data into groups of similar objects. Each group, called a cluster, consists of objects that are similar between themselves and dissimilar compared to objects of other groups. This paper is intended to study and compare different data clustering algorithms. The algorithms under investigation are: k-means algorithm, hierarchical clustering algorithm, self-organizing ma...
متن کاملEmpirical Evaluation of Clustering Algorithms*
Unsupervised data classification can be considered one of the most important initial steps in the process of data mining. Numerous algorithms have been developed and are being used in this context in a variety of application domains. Albeit, only little evidence is available as to which algorithms should be used in which context, and which techniques offer promising results when being combined ...
متن کاملAn Empirical Research of Dynamic Clustering Algorithms
Clustering and visualizing high dimensional dynamic data is a challenging problem in the data mining. Most of the existing clustering algorithms are based on the static statistical relationship among data. In the clustering process there are no predefined classes and no examples that would show what kind of desirable relations should be valid among the data. This paper gives existing work done ...
متن کاملClustering of a Number of Genes Affecting in Milk Production using Information Theory and Mutual Information
Information theory is a branch of mathematics. Information theory is used in genetic and bioinformatics analyses and can be used for many analyses related to the biological structures and sequences. Bio-computational grouping of genes facilitates genetic analysis, sequencing and structural-based analyses. In this study, after retrieving gene and exon DNA sequences affecting milk yield in dairy ...
متن کاملThe ensemble clustering with maximize diversity using evolutionary optimization algorithms
Data clustering is one of the main steps in data mining, which is responsible for exploring hidden patterns in non-tagged data. Due to the complexity of the problem and the weakness of the basic clustering methods, most studies today are guided by clustering ensemble methods. Diversity in primary results is one of the most important factors that can affect the quality of the final results. Also...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Fuzzy Logic and Intelligent Systems
سال: 2010
ISSN: 1598-2645
DOI: 10.5391/ijfis.2010.10.1.031