Clustering Similarity Comparison Using Density Profiles

Bae, Eric; Bailey, James E.; Dong, Guozhu

doi:10.1007/11941439_38

Cited by 8 publications

(8 citation statements)

References 16 publications

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“…There are other measures for similarity between clustering structures that might be used to develop a statistical test. However, these similarity measures cannot be extended in a straightforward manner to simultaneously test more than two populations.…”

Section: Final Remarksmentioning

confidence: 99%

A non‐parametric statistical test to compare clusters with applications in functional magnetic resonance imaging data

Fujita

Takahashi

Patriota

et al. 2014

Statistics in Medicine

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Statistical inference of functional magnetic resonance imaging (fMRI) data is an important tool in neuroscience investigation. One major hypothesis in neuroscience is that the presence or not of a psychiatric disorder can be explained by the differences in how neurons cluster in the brain. Therefore, it is of interest to verify whether the properties of the clusters change between groups of patients and controls. The usual method to show group differences in brain imaging is to carry out a voxel-wise univariate analysis for a difference between the mean group responses using an appropriate test and to assemble the resulting 'significantly different voxels' into clusters, testing again at cluster level. In this approach, of course, the primary voxel-level test is blind to any cluster structure. Direct assessments of differences between groups at the cluster level seem to be missing in brain imaging. For this reason, we introduce a novel non-parametric statistical test called analysis of cluster structure variability (ANOCVA), which statistically tests whether two or more populations are equally clustered. The proposed method allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering. We illustrate the performance of ANOCVA through simulations and an application to an fMRI dataset composed of children with attention deficit hyperactivity disorder (ADHD) and controls. Results show that there are several differences in the clustering structure of the brain between them. Furthermore, we identify some brain regions previously not described to be involved in the ADHD pathophysiology, generating new hypotheses to be tested. The proposed method is general enough to be applied to other types of datasets, not limited to fMRI, where comparison of clustering structures is of interest.

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Section: Final Remarksmentioning

confidence: 99%

A non‐parametric statistical test to compare clusters with applications in functional magnetic resonance imaging data

Fujita

Takahashi

Patriota

et al. 2014

Statistics in Medicine

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“…This paper is an expanded version of work in [1], which first described the ADCO measure. Compared to that work, this paper contains the following additional material: i) gives a deeper analysis of the philosophy behind ADCO and proves a number of formal properties of the measure, ii) presents a more comprehensive experimental analysis of its accuracy, compared to other measures, using more datasets, iii) analyses ADCO's runtime complexity both formally and experimentally, iv) shows how ADCO can be used in a novel way, as an objective measure for a powerful new alternate clustering algorithm called MAXIMUS.…”

Section: Related Workmentioning

confidence: 99%

A clustering comparison measure using density profiles and its application to the discovery of alternate clusterings

Bae

Bailey

Dong

2010

Data Min Knowl Disc

Self Cite

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Data clustering is a fundamental and very popular method of data analysis. Its subjective nature, however, means that different clustering algorithms or different parameter settings can produce widely varying and sometimes conflicting results. This has led to the use of clustering comparison measures to quantify the degree of similarity between alternative clusterings. Existing measures, though, can be limited in their ability to assess similarity and sometimes generate unintuitive results. They also cannot be applied to compare clusterings which contain different data points, an activity which is important for scenarios such as data stream analysis.In this paper, we introduce a new clustering similarity measure, known as ADCO, which aims to address some limitations of existing measures, by allowing greater flexibility of comparison via the use of density profiles to characterize a clustering. In particular, it adopts a 'data mining style' philosophy to clustering comparison, whereby two clusterings are considered to be more similar, if they are likely to give rise to similar types of prediction models.Furthermore, we show that this new measure can be applied as a highly effective objective function within a new algorithm, known as MAXIMUS, for generating alternate clusterings.

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“…Finally, membership distribution methods compute the distance between the cluster membership distributions of the two clusterings. Examples include ADCO [30,31].…”

Section: External Accuracy Validation Measurementioning

confidence: 99%

Using graph partitioning to discover regions of correlated spatio-temporal change in evolving graphs

Chan

Bailey

2009

IDA

Self Cite

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There is growing interest in studying dynamic graphs, or graphs that evolve with time. In this work, we investigate a new type of dynamic graph analysis -finding regions of a graph that are evolving in a similar manner and are topologically similar over a period of time. For example, these regions can be used to group a set of changes having a common cause in event detection and fault diagnosis. Prior work [6] has proposed a greedy framework called cSTAG to find these regions. It was accurate in datasets where the regions are temporally and spatially well separated. However, in cases where the regions are not well separated, cSTAG produces incorrect groupings. In this paper, we propose a new algorithm called regHunter. It treats the region discovery problem as a multi-objective optimisation problem, and it uses a multi-level graph partitioning algorithm to discover the regions of correlated change. In addition, we propose an external clustering validation technique, and use several existing internal measures to evaluate the accuracy of regHunter. Using synthetic datasets, we found regHunter is significantly more accurate than cSTAG in dynamic graphs that have regions with small separation. Using two real datasets -the access graph of the 1998 World Cup website, and the BGP connectivity graph during the landfall of Hurricane Katrina -we found regHunter obtained more accurate results than cSTAG. Furthermore, regHunter was able to discover two interesting regions for the World Cup access graph that CSTAG was not able to find.

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Clustering Similarity Comparison Using Density Profiles

Cited by 8 publications

References 16 publications

A non‐parametric statistical test to compare clusters with applications in functional magnetic resonance imaging data

A non‐parametric statistical test to compare clusters with applications in functional magnetic resonance imaging data

A clustering comparison measure using density profiles and its application to the discovery of alternate clusterings

Using graph partitioning to discover regions of correlated spatio-temporal change in evolving graphs

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