Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

7

PKDD
2015
Springer

favoriteEmaildiscussreport

15views Data Mining» more PKDD 2015»

Fuzzy Clustering of Series Using Quantile Autocovariances

8 years 8 days ago

Fuzzy Clustering of Series Using Quantile Autocovariances

Download ceur-ws.org

Unlike conventional clustering, fuzzy cluster analysis allows data elements to belong to more than one cluster by assigning membership degrees of each data to clusters. This work proposes a fuzzy C– medoids algorithm to cluster time series based on comparing their estimated quantile autocovariance functions. The behaviour of the proposed algorithm is studied on diﬀerent simulated scenarios and its eﬀectiveness is concluded by comparison with alternative approaches.

Borja R. Lafuente-Rego, José Antonio Vilar

Real-time Traffic

Data Mining | PKDD 2015 |

claim paper

Related Content

» Fuzzy Clustering Based Segmentation of TimeSeries

» Data Mining for extraction of fuzzy IFTHEN rules using Mamdani and TakagiSugenoKang FIS

» Machine learning approaches for estimation of prediction interval for the model output

» A Fuzzy Set Approach to Ecological Knowledge Discovery

» Preventing Meaningless Stock Time Series Pattern Discovery by Changing Perceptually Import...

» Identification of Fuzzy Inference System Based on Information Granulation

» Forecasting Exchange Rates A NeuroFuzzy Approach

» A novel series of compositionally biased substitution matrices for comparing Plasmodium pr...

» Systematic gene function prediction from gene expression data by using a fuzzy nearestclus...

» Dominant SetsBased Action Recognition using Image Sequence Matching

Post Info
More Details (n/a)

Added	16 Apr 2016
Updated	16 Apr 2016
Type	Journal
Year	2015
Where	PKDD
Authors	Borja R. Lafuente-Rego, José Antonio Vilar

Comments (0)