Information Theory and Voting Based Consensus Clustering for Combining Multiple Clusterings of Chemical Structures

Saeed, Faisal; Salim, Naomie; Abdo, Ammar

doi:10.1002/minf.201300004

Cited by 13 publications

(7 citation statements)

References 34 publications

(45 reference statements)

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“…The key information of interest is often obscured behind redundancy and noise, and grouping the data into clusters with similar features is one way of efficiently summarizing the data for further analysis [ 1 ]. Cluster analysis has been used in many fields [ 1 , 2 ], such as information retrieval [ 3 ], social media analysis [ 4 ], neuroscience [ 5 ], image processing [ 6 ], text analysis [ 7 ] and bioinformatics [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm

et al. 2016

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The K-means algorithm is one of the most popular clustering algorithms in current use as it is relatively fast yet simple to understand and deploy in practice. Nevertheless, its use entails certain restrictive assumptions about the data, the negative consequences of which are not always immediately apparent, as we demonstrate. While more flexible algorithms have been developed, their widespread use has been hindered by their computational and technical complexity. Motivated by these considerations, we present a flexible alternative to K-means that relaxes most of the assumptions, whilst remaining almost as fast and simple. This novel algorithm which we call MAP-DP (maximum a-posteriori Dirichlet process mixtures), is statistically rigorous as it is based on nonparametric Bayesian Dirichlet process mixture modeling. This approach allows us to overcome most of the limitations imposed by K-means. The number of clusters K is estimated from the data instead of being fixed a-priori as in K-means. In addition, while K-means is restricted to continuous data, the MAP-DP framework can be applied to many kinds of data, for example, binary, count or ordinal data. Also, it can efficiently separate outliers from the data. This additional flexibility does not incur a significant computational overhead compared to K-means with MAP-DP convergence typically achieved in the order of seconds for many practical problems. Finally, in contrast to K-means, since the algorithm is based on an underlying statistical model, the MAP-DP framework can deal with missing data and enables model testing such as cross validation in a principled way. We demonstrate the simplicity and effectiveness of this algorithm on the health informatics problem of clinical sub-typing in a cluster of diseases known as parkinsonism.

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Section: Introductionmentioning

confidence: 99%

What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm

et al. 2016

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“…Although the CVAA can outperform Ward’s method and give better results than other consensus clusterings, as shown by Saeed et al, different results are obtained every time the CVAA is applied because the final partition depends on the arrangement of partitions during the relabeling process. Therefore, the adaptive cumulative voting-based aggregation algorithm (A-CVAA) was used to overcome this limitation …”

Section: Methodsmentioning

confidence: 99%

“…Moreover, Saeed et al 25 used a cumulative voting-based aggregation algorithm (CVAA) for combining multiple clusterings of chemical structures and found that it could significantly improve the quality of clustering. In addition, an adaptive voting-based consensus method was used by Saeed et al 26 to obtain a final consensus partition with a greater quantity of mutual information associated with its clusters. However, an enhanced votingbased consensus method was introduced in this paper and compared with other consensus clustering methods (four coassociation-based, two graph-based, and two voting-based methods) and to the Ward's method to study the effectiveness of consensus methods for combining multiple clusterings of chemical structures.…”

Section: Introductionmentioning

confidence: 99%

Consensus Methods for Combining Multiple Clusterings of Chemical Structures

Saeed

Salim

Abdo

2013

J. Chem. Inf. Model.

Self Cite

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The goal of consensus clustering methods is to find a consensus partition that optimally summarizes an ensemble and improves the quality of clustering compared with single clustering algorithms. In this paper, an enhanced voting-based consensus method was introduced and compared with other consensus clustering methods, including co-association-based, graph-based, and voting-based consensus methods. The MDDR and MUV data sets were used for the experiments and were represented by three 2D fingerprints: ALOGP, ECFP_4, and ECFC_4. The results were evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster using four criteria: F-measure, Quality Partition Index (QPI), Rand Index (RI), and Fowlkes-Mallows Index (FMI). The experiments suggest that the consensus methods can deliver significant improvements for the effectiveness of chemical structures clustering.

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“…Clustering analysis aims to detect the nature groups or clusters of data objects in attributes space, and it is one of the most important techniques in data mining [1], [2]. Clustering algorithms are used in a wide range of fields such as social media analysis [3], information retrieval [4], [5], image analysis [6], privacy preserving [7], text analysis [8], and bioinformatics [9], [10]. The goal of clustering is to group data objects into clusters such that the data objects in the same cluster are as similar as possible and the ones from different clusters are as dissimilar as possible [11].…”

Section: Introductionmentioning

confidence: 99%

Clustering Mixed Numeric and Categorical Data With Cuckoo Search

Pang

et al. 2020

IEEE Access

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Clustering analysis, as an important technique in data mining, aims to identify the nature groups or clusters of data objects in the attribute space. Data objects in real-world applications are commonly described by both numeric and categorical attributes. In this research, considering that the partitional clustering algorithms designed for this type of mixed data are prone to get trapped into local optima and the cuckoo search approach is efficient in solving global optimization problems, we propose CCS-K-Prototypes, a novel partitional Clustering algorithm based on Cuckoo Search and K-Prototypes, for clustering mixed numeric and categorical data. To deal with different types of attributes, we develop a novel representation for candidate solutions, and suggest two formulas for the cuckoo to search for the potential solution around the existing solutions or in the entire attribute space. Finally, the performance of the proposed algorithm is assessed by a series of experiments on five benchmark datasets. INDEX TERMS Data clustering, cuckoo search, mixed data, numeric and categorical attributes.

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Information Theory and Voting Based Consensus Clustering for Combining Multiple Clusterings of Chemical Structures

Cited by 13 publications

References 34 publications

What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm

What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm

Consensus Methods for Combining Multiple Clusterings of Chemical Structures

Clustering Mixed Numeric and Categorical Data With Cuckoo Search

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