A taxonomy of clustering procedures
نویسندگان
چکیده
In the field of multicriteria decision aid, considerable attention has been paid to supervised classification problems, especially to so-called sorting problems, where an order is assumed on the predefined classes. Recently, some non-supervised multicriteria classification procedures, also known as multicriteria clustering procedures, have been proposed aiming to discover data structures from a multicriteria perspective. We enlighten some properties of such approaches and their differences with regards to classical procedures, and we propose a taxonomy of this family of procedures. Moreover, we analyze extend to which these procedures differ from the multicriteria ranking problematic.
منابع مشابه
A STUDY OF THE GENUS ISATIS IN IRAN
This paper presents the findings of a research on the genus Isatis L.(Brassicaceae) in Iran. Isatis specimens kept in major herbaria of Iran were examined carefully. Fresh specimens were collected from Tehran, Mazandaran, Semnan, Hamedan, Kurdistan, W. Azarbaijan , E. Azarbaijan, Ardebil and Ghazvin provinces. Images of type or otherwise valid specimens kept in Viena, Munich, Montpollier and Ke...
متن کاملComparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text
The application of clustering methods for automatic taxonomy construction from text requires knowledge about the tradeoff between, (i), their effectiveness (quality of result), (ii), efficiency (run-time behaviour), and, (iii), traceability of the taxonomy construction by the ontology engineer. In this line, we present an original conceptual clustering method based on Formal Concept Analysis fo...
متن کاملIterative Rank based Methods for Clustering
Recently a new clustering algorithm was developed, useful in phylogenetic systematics and taxonomy. It derives a hierarchy from (dis)similarity data on a simple and rather natural way. It transforms a given dissimilarity by an iterative approach. Each iteration step consists of ranking the objects under consideration according to their pairwise dissimilarity and calculating the Euclidian distan...
متن کاملTaxonomy Inference Using Kernel Dependence Measures
We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the clusters. The algorithms work by maximizing the dependence between the taxonomy and the original data. The resulting taxonomy is a more informative visualization of complex data than simple clustering; in addition, tak...
متن کاملComparing Conceptual, Divisive and Agglomerative Clustering for Learning Taxonomies from Text
The application of clustering methods for automatic taxonomy construction from text requires knowledge about the tradeoff between, (i), their effectiveness (quality of result), (ii), efficiency (run-time behaviour), and, (iii), traceability of the taxonomy construction by the ontology engineer. In this line, we present an original conceptual clustering method based on Formal Concept Analysis fo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2016