Distributed Identification of Top-l Inner Product Elements and its Application in a Peer-to-Peer Network

The inner product measures how closely two feature vectors are related. It is an important primitive for many popular data mining tasks, for example, clustering, classification, correlation computation, and decision tree construction. If the entire data set is available at a single site, then comput...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on knowledge and data engineering Jg. 20; H. 4; S. 475 - 488
Hauptverfasser:	Das, K., Bhaduri, K., Kun Liu, Kargupta, H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.04.2008 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Algorithms for data and knowledge management Applied sciences Classification tree analysis Computation Computer science; control theory; systems Computer systems and distributed systems. User interface Data mining Data processing. List processing. Character string processing Decision trees Exact sciences and technology Knowledge management applications Large-scale systems Mathematical analysis Memory organisation. Data processing Mining methods and algorithms Network servers Networks Partitioning algorithms Peer to peer computing Probabilistic algorithms Scalability Software Statistical distributions Statistics Studies Vectors (mathematics) Probabilistic algorithms Knowledge management applications Algorithms for data and knowledge management Data mining Mining methods and algorithms Cluster analysis Distributed data mining Data analysis Correlation Peer to peer Statistical analysis Probabilistic approach Scalability Matrix product pier-to-peer network Information extraction Distributed system Decision tree Modeling Classification inner product Localization
ISSN:	1041-4347, 1558-2191
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!