Robust, Complete, and Efficient Correlation Clustering

Achtert, Elke; Böhm, Christian; Kriegel, Hans‐Peter; Kröger, Peer; Zimek, Arthur

doi:10.1137/1.9781611972771.37

Cited by 52 publications

(46 citation statements)

References 17 publications

(12 reference statements)

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“…In our experiments we use the correlation clustering algorithm COPAC [1] to generate the correlation clusters in a preprocessing step to our method. We choose this algorithm due to its efficiency, effectivity, and robustness.…”

Section: Discussionmentioning

confidence: 99%

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Deriving quantitative models for correlation clusters

Achtert

Böhm

Kriegel

et al. 2006

Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Correlation clustering aims at grouping the data set into correlation clusters such that the objects in the same cluster exhibit a certain density and are all associated to a common arbitrarily oriented hyperplane of arbitrary dimensionality. Several algorithms for this task have been proposed recently. However, all algorithms only compute the partitioning of the data into clusters. This is only a first step in the pipeline of advanced data analysis and system modelling. The second (post-clustering) step of deriving a quantitative model for each correlation cluster has not been addressed so far. In this paper, we describe an original approach to handle this second step. We introduce a general method that can extract quantitative information on the linear dependencies within a correlation clustering. Our concepts are independent of the clustering model and can thus be applied as a post-processing step to any correlation clustering algorithm. Furthermore, we show how these quantitative models can be used to predict the probability distribution that an object is created by these models. Our broad experimental evaluation demonstrates the beneficial impact of our method on several applications of significant practical importance.

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Section: Discussionmentioning

confidence: 99%

“…The decision for a specific clustering algorithm will also determine whether or not a data object may belong to several clusters simultaneously. In our experiments we use COPAC [1], a new correlation clustering algorithm that is shown to improve over 4C as well as ORCLUS w.r.t. efficiency, effectivity, and robustness.…”

Section: Deriving Quantitative Models Formentioning

confidence: 99%

Deriving quantitative models for correlation clusters

Achtert

Böhm

Kriegel

et al. 2006

Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Self Cite

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show abstract

“…The algorithms define their own heuristics to distinguish between dense and non dense space regions, but they usually rely on user defined density thresholds. Some recent examples of density based algorithms are COPAC [1], STATPC [14] and MrCC [5].…”

Section: Clusteringmentioning

confidence: 99%

“…Figure 1 illustrates the research problems and the QMAS results. Figure 1(a) is a sample satellite image from the city of Annapolis, MD, USA 1 . We decomposed it into 1, 024 (32x32) tiles, very few (4) of which were manually labeled as "City" (red), "Water" (cyan), "Urban Trees" (green) or "Forest" (black).…”

Section: Introductionmentioning

confidence: 99%

QMAS: Querying, Mining and Summarization of Multi-modal Databases

Cordeiro

Guo

Haverkamp³

et al. 2010

2010 IEEE International Conference on Data Mining

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Given a large collection of images, very few of which have labels given a priori, how can we automatically assign the labels of the remaining majority, and make suggestion for images that may need brand new labels distinct from existing ones? Popular automatic labeling techniques usually scale super linearly with the size of the image set, and/or their performances degrade if limited images bear initial labels. In this paper, we propose QMAS, an efficient solution to the following problems: (i) low-labor labeling (L3) -given a collection of images, very few of which are already labeled with keywords, find the most suitable labels for the remaining ones; and (ii) mining and attention routing -with the same input set, output a number of top representative images and top outliers. We present experimental evaluation on three data sets of proprietary and public satellite images up to a size of 2.25GB. QMAS scales linearly with the number of images, obtaining better or equal accuracy while being up to 40 times faster than its baseline algorithm. With limited numbers of initial labels available, QMAS achieves a significant accuracy margin over the baseline approach. The application of QMAS to recommend representatives and spot outliers is also illustrated. The proposed framework could be generalized to solve similar content-based annotation and mining problems on other multi-modal databases.

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“…There are axis-parallel subspace and projected clustering approaches implemented like CLIQUE [11], PROCLUS [12], SUBCLU [13], PreDeCon [14], HiSC [15], and DiSH [16]. Furthermore, some biclustering or pattern-based clustering approaches are supported like δ-bicluster [17], FLOC [18] or p-cluster [19], and correlation clustering approaches are incorporated like ORCLUS [20], 4C [21], HiCO [22], COPAC [23], ERiC [24], and CASH [25]. The improvements on these algorithms described in [26] are also integrated in ELKI.…”

Section: Available Algorithmsmentioning

confidence: 99%

ELKI: A Software System for Evaluation of Subspace Clustering Algorithms

Achtert

Kriegel

Zimek

Lecture Notes in Computer Science

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Abstract. In order to establish consolidated standards in novel data mining areas, newly proposed algorithms need to be evaluated thoroughly. Many publications compare a new proposition -if at all -with one or two competitors or even with a so called "naïve" ad hoc solution. For the prolific field of subspace clustering, we propose a software framework implementing many prominent algorithms and, thus, allowing for a fair and thorough evaluation. Furthermore, we describe how new algorithms for new applications can be incorporated in the framework easily.

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Robust, Complete, and Efficient Correlation Clustering

Cited by 52 publications

References 17 publications

Deriving quantitative models for correlation clusters

Deriving quantitative models for correlation clusters

QMAS: Querying, Mining and Summarization of Multi-modal Databases

ELKI: A Software System for Evaluation of Subspace Clustering Algorithms

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