A principled and flexible framework for finding alternative clusterings

Qi, Zijie; Davidson, Ian

doi:10.1145/1557019.1557099

Cited by 81 publications

(57 citation statements)

References 12 publications

(12 reference statements)

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“…Then we combined the detected subspace clusters by DOC from all runs as the final result. For competing multi-view methods we selected Multivew1 and Multivew2 proposed in [5] and two variants AltClus1 and AltClus2 of the Alternative Clustering methods proposed in [14]. Since these methods do not generate subspace information, we ignored the subspaces during the evaluation.…”

Section: Preliminary Experimentsmentioning

confidence: 99%

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Stochastic subspace search for top-k multi-view clustering

Günnemann

Zaki

2013

Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-View Data, and Multi-Source Knowledge-Driven Clusteri

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Finding multiple clustering solutions has recently gained much attention. Based on the observation that data is often multi-faceted, novel clustering methods have been introduced capable of detecting multiple, diverse clusterings. In this work-in-progress paper, we present a novel stochastic subspace search principle that tackles the requirements of multi-view clustering. The main idea is to consider each subspace as a state in a Markov chain and using Monte Carlo methods to sample the multi-view subspaces. By dynamically adapting the underlying probability density function we realize the generation of alternative clustering views. We present preliminary experimental results of our method and we describe future research directions.

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Section: Preliminary Experimentsmentioning

confidence: 99%

“…According to [11], they can briefly be categorized into methods operating on the original (full-dimensional) dataspace [6], methods performing space transformations [5,14], and methods analyzing (axis-parallel) subspace projections [9,8]. In this work-in-progress paper, we describe a novel method belonging to the last category.…”

Section: Introductionmentioning

confidence: 99%

Stochastic subspace search for top-k multi-view clustering

Günnemann

Zaki

2013

Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-View Data, and Multi-Source Knowledge-Driven Clusteri

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“…A large body of work studies the problem of discovering multiple clustering solutions [26], [27], [28], [29], [30]. The objective in these papers is to discover multiple clusterings for a given dataset.…”

Section: Related Workmentioning

confidence: 99%

Overlapping correlation clustering

2012

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Abstract-We introduce a new approach to the problem of overlapping clustering. The main idea is to formulate overlapping clustering as an optimization problem in which each data point is mapped to a small set of labels, representing membership to different clusters. The objective is to find a mapping so that the distances between data points agree as much as possible with distances taken over their label sets. To define distances between label sets, we consider two measures: a set-intersection indicator function and the Jaccard coefficient.To solve the main optimization problem we propose a localsearch algorithm. The iterative step of our algorithm requires solving non-trivial optimization subproblems, which, for the measures of set-intersection and Jaccard, we solve using a greedy method and non-negative least squares, respectively.Since our frameworks uses pairwise similarities of objects as the input, it lends itself naturally to the task of clustering structured objects for which feature vectors can be difficult to obtain. As a proof of concept we show how easily our framework can be applied in two different complex application domains. Firstly, we develop overlapping clustering of animal trajectories, obtaining zoologically meaningful results. Secondly, we apply our framework for overlapping clustering of proteins based on pairwise similarities of aminoacid sequences, outperforming the of state-of-the-art method in matching a ground truth taxonomy.

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“…• The idea of disparate clustering through a relation is closely connected to the current topic of mining multiple, alternative, clusterings [1,18]. Alternative clusterings are to be expected in high dimensional datasets where different explanations of the data may involve using distinct subspaces of the data.…”

Section: Introductionmentioning

confidence: 99%

Unifying dependent clustering and disparate clustering for non-homogeneous data

Hossain

Tadepalli

Watson

et al. 2010

Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Modern data mining settings involve a combination of attributevalued descriptors over entities as well as specified relationships between these entities. We present an approach to cluster such non-homogeneous datasets by using the relationships to impose either dependent clustering or disparate clustering constraints. Unlike prior work that views constraints as boolean criteria, we present a formulation that allows constraints to be satisfied or violated in a smooth manner. This enables us to achieve dependent clustering and disparate clustering using the same optimization framework by merely maximizing versus minimizing the objective function. We present results on both synthetic data as well as several real-world datasets.

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A principled and flexible framework for finding alternative clusterings

Cited by 81 publications

References 12 publications

Stochastic subspace search for top-k multi-view clustering

Stochastic subspace search for top-k multi-view clustering

Overlapping correlation clustering

Unifying dependent clustering and disparate clustering for non-homogeneous data

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