Clustering Ensemble Selection Considering Quality and Diversity
نویسندگان
چکیده
منابع مشابه
Clustering Ensemble Selection Considering Quality and Diversity
Information clustering means classifying information or partitioning some samples in clusters such that samples inside each cluster have maximum similarity to each other and maximum distance from other clusters. As clustering is unsupervised, selecting a specific algorithm for clustering of an unknown set may fail. As a consequence of problem complexity and deficiencies in basic clustering meth...
متن کاملA new ensemble clustering method based on fuzzy cmeans clustering while maintaining diversity in ensemble
An ensemble clustering has been considered as one of the research approaches in data mining, pattern recognition, machine learning and artificial intelligence over the last decade. In clustering, the combination first produces several bases clustering, and then, for their aggregation, a function is used to create a final cluster that is as similar as possible to all the cluster bundles. The inp...
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 کاملDiversity in Ensemble Feature Selection
Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown theoretically and experimentally that in order for an ensemble to be effective, it should consist of high-accuracy base classifiers that should have high diversity in their pred...
متن کاملEnsemble Selection Using Diversity Networks
An ideal ensemble is composed of base classifiers that perform well and that have minimal overlap in their errors. Eliminating classifiers from an ensemble based on a criterion that reflects poor classification performance and error redundancy with peer classifiers can improve ensemble performance. The Diversity Networks method asymmetrically evaluates each pair of classifiers as a linear combi...
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: Research in Computing Science
سال: 2015
ISSN: 1870-4069
DOI: 10.13053/rcs-102-1-8