VALIDATION OF CLUSTERING METHODS FOR MEDICAL DATA SETS
نویسندگان
چکیده
منابع مشابه
Clustering of Fuzzy Data Sets Based on Particle Swarm Optimization With Fuzzy Cluster Centers
In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy ...
متن کاملEntropy-based Consensus for Distributed Data Clustering
The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...
متن کاملSupervised sampling for clustering large data sets
The problem of clustering large data sets has attracted a lot of current research. The approaches taken are mainly based either on the more efficient implementation or modification of existing methods or/and on the construction of clusters from a small sub-sample of the data and then the assignment of all observations in those clusters. The current paper focuses on the latter direction. An alte...
متن کاملClustering Algorithm for Arbitrary Data Sets
Clustering analysis is an intrinsic component of numerous applications, including pattern recognition, life sciences, image processing, web data analysis, earth sciences, and climate research. As an example, consider the biology domain. In any living cell that undergoes a biological process, different subsets of its genes are expressed in different stages of the process. To facilitate a deeper ...
متن کاملA Clustering Algorithm for Logfile Data Sets
Today, vast amounts of system status and health information are stored in logfiles. Therefore, mining patterns from logfiles is an important system management task. This paper presents a novel clustering algorithm for logfile data sets which helps one to detect frequent patterns from logfiles, to build logfile models, and to identify anomalous logfile lines.
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: Acta Healthmedica
سال: 2017
ISSN: 2414-6528
DOI: 10.19082/ah116