CVAP: Validation for Cluster Analyses

Wang, Kaijun; Wang, Baijie; Peng, Liuqing

doi:10.2481/dsj.007-020

Cited by 139 publications

(62 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The presentation is usually in some graphical format, hence these tools may be labelled as "visual" cluster validation tools. One such tool is CVAP (Cluster Validity Analysis Platform) [24]. CVAP operates by applying a number of clustering algorithms, with a sequence of parameters, to a given data set.…”

Section: Previous Workmentioning

confidence: 99%

Best Clustering Configuration Metrics: Towards Multiagent Based Clustering

Chaimontree

Atkinson

Coenen

2010

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Multi-Agent Clustering (MAC) requires a mechanism for identifying the most appropriate cluster configuration. This paper reports on experiments conducted with respect to a number of validation metrics to identify the most effective metric with respect to this context. This paper also describes a process whereby such metrics can be used to determine the optimum parameters typically required by clustering algorithms, and a process for incorporating this into a MAC framework to generate best cluster configurations with minimum input from end users.

show abstract

Section: Previous Workmentioning

confidence: 99%

Best Clustering Configuration Metrics: Towards Multiagent Based Clustering

Chaimontree

Atkinson

Coenen

2010

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…This way, we expect to have a cluster of roads with high prediction accuracy and vice versa. This problem is usually referred as cluster validation [36], [37].…”

Section: A Analysis Of Spatial Prediction Patternsmentioning

confidence: 99%

Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction

Asif

Dauwels

Goh

et al. 2014

IEEE Trans. Intell. Transport. Syst.

187

View full text Add to dashboard Cite

Abstract-The ability to accurately predict traffic speed in a large and heterogeneous road network has many useful applications, such as route guidance and congestion avoidance. In principle, data driven methods such as Support Vector Regression (SVR) can predict traffic with high accuracy, because traffic tends to exhibit regular patterns over time. However, in practice, the prediction performance can vary significantly across the network and during different time periods. Insight into those spatial and temporal trends can improve the performance of Intelligent Transportation Systems (ITS). Traditional prediction error measures such as Mean Absolute Percentage Error (MAPE) provide information about individual links in the network, but do not capture global trends. We propose unsupervised learning methods, such as k-means clustering, Principal Component Analysis (PCA), and Self Organizing Maps (SOM) to mine spatial and temporal performance trends at both network level and for individual links. We perform prediction for a large, interconnected road network, for multiple prediction horizons, with SVR based algorithm. We show the effectiveness of the proposed performance analysis methods by applying them to the prediction data of SVR.Index Terms-Large-scale network prediction, spatial and temporal error trends.

show abstract

“…In order to measure the agreement between the ground truth labels and the two clustering procedures, we computed four different indices of external validity, that is: Rand index, adjusted Rand index, Jaccard index, Fowlkes-Mallows index [24]; furthermore, we calculate an error rate, which is the percentage of error class labels of the clustering solution compared with true labels, adopting the figure of merits suggested in [25]. The results are shown in Fig.…”

Section: Spectators Segmentation and Excitement Calculationmentioning

confidence: 99%

ATTENTO: ATTENTion Observed for Automated Spectator Crowd Analysis

Conigliaro

Setti

Bassetti

et al. 2013

Human Behavior Understanding

View full text Add to dashboard Cite

Abstract. We propose a new type of crowd analysis, focused on the spectator crowd, that is, people "interested in watching something specific that they came to see" [1]. This scenario applies on stadiums, amphitheaters etc., and shares some aspects with classical crowd monitoring: actually, many people are simultaneously observed, so that perperson analysis is hard; however, here the dynamics of humans is more constrained, due to the architectural environment in which they are situated; specifically, people are expected to stay in a fixed location most of the time, limiting their activities to applaud, support/heckle the players or discuss with the neighbors. In this paper, we start facing this challenge by considering hockey matches, locating a videocamera 25-30 meters far from the bleachers, pointing at the crowd: in this scenario, aggregations of spectators that exhibit similar behavior are detected, and the behavior is classified into a set of predefined classes, highlighting the overall excitement. To these aims, in a first step we focus on individual frames, clustering local flow measures into spatial regions. The clustering is then extended by adding the temporal axis into the analysis, looking for non-randomic spatio-temporal clusters; to this aim, the Lempel-Ziv complexity is considered. This way, choral activities can emerge, indicating for example fan groups belonging to different teams. After this, with the adoption of entropic measures, the degree of excitement of such groups can be quantified.

show abstract

CVAP: Validation for Cluster Analyses

Abstract: Evaluation of clustering results (or cluster validation) is an important and necessary step in cluster analysis

Cited by 139 publications

References 13 publications

Best Clustering Configuration Metrics: Towards Multiagent Based Clustering

Best Clustering Configuration Metrics: Towards Multiagent Based Clustering

Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction

ATTENTO: ATTENTion Observed for Automated Spectator Crowd Analysis

Contact Info

Product

Resources

About