Deriving quantitative models for correlation clusters

Elke Achtert*, Christian Böhm, Hans Peter Kriegel, Peer Kröger, Arthur Zimek

*Kontaktforfatter for dette arbejde

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Abstrakt

Correlation clustering aims at grouping the data set into correlation clusters such that the objects in the same cluster exhibit a certain density and are all associated to a common arbitrarily oriented hyperplane of arbitrary dimensionality. Several algorithms for this task have been proposed recently. However, all algorithms only compute the partitioning of the data into clusters. This is only a first step in the pipeline of advanced data analysis and system modelling. The second (post-clustering) step of deriving a quantitative model for each correlation cluster has not been addressed so far. In this paper, we describe an original approach to handle this second step. We introduce a general method that can extract quantitative information on the linear dependencies within a correlation clustering. Our concepts are independent of the clustering model and can thus be applied as a post-processing step to any correlation clustering algorithm. Furthermore, we show how these quantitative models can be used to predict the probability distribution that an object is created by these models. Our broad experimental evaluation, demonstrates the beneficial impact of our method on several applications of significant practical importance.

OriginalsprogEngelsk
TitelKDD 2006 : Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
ForlagAssociation for Computing Machinery
Publikationsdato20. aug. 2006
Sider4-13
ISBN (Trykt)1-59593-339-5
DOI
StatusUdgivet - 20. aug. 2006
Udgivet eksterntJa
BegivenhedKDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Philadelphia, PA, USA
Varighed: 20. aug. 200623. aug. 2006

Konference

KonferenceKDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
LandUSA
ByPhiladelphia, PA
Periode20/08/200623/08/2006
SponsorACM SIGKDD, ACM SIGMOD

    Fingerprint

Citationsformater

Achtert, E., Böhm, C., Kriegel, H. P., Kröger, P., & Zimek, A. (2006). Deriving quantitative models for correlation clusters. I KDD 2006: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (s. 4-13). Association for Computing Machinery. https://doi.org/10.1145/1150402.1150408