Abstract
Abstract Clustering is one of the most fundamental and essential data analysis tasks with broad applications. It has been studied in various research fields: data mining, machine learning, pattern recognition and in engineering, economics and biomedical data analysis. Headache is not a disease that typically shortens one’s life, but it can be a serious social as well as a health problem. Approximately 27 billion euros per year are lost through reduced work productivity in the European community. This paper is focused on a new strategy based on a hybrid model for combining fuzzy partition method and maximum likelihood estimation clustering algorithm for diagnosing primary headache disorder. The proposed hybrid system is tested on two data sets for diagnosing headache disorder collected from Clinical Centre of Vojvodina in Serbia.
Highlights
Clustering is one of the most fundamental and essential data analysis tasks with broad applications
This paper is focused on a new strategy based on a hybrid model for combining fuzzy partition method and maximum likelihood estimation clustering algorithm for diagnosing primary headache disorder
This paper presents hybrid clustering approach for diagnosing primary headache disorder combining fuzzy partition method and maximum likelihood estimation clustering algorithm
Summary
Clustering is one of the most fundamental and essential data analysis tasks with broad applications. It is a process in which a group of unlabeled patterns are partitioned into several sets so that similar patterns are assigned to the same cluster, and dissimilar patterns are assigned to different clusters. The purpose of clustering is to identify natural groupings of data from a large data set to produce a concise representation of a system’s behaviour. There are some goals for clustering algorithms: (1) estimating the optimal number of clusters, (2) determining good clusters and (3) doing so efficiently. One of the main difficulties for cluster analysis is estimating the optimal and the correct number of clusters of different types of data sets
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
