A probabilistic framework for relational clustering

Long, Bo; Zhang, Zhongfei Mark; Yu, Philip S.

doi:10.1145/1281192.1281244

Cited by 94 publications

(59 citation statements)

References 39 publications

(84 reference statements)

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“…al. [23,24,25], and Bregman tensor clustering [5] (which can handle higher arity relations). Matrix analogues of factor analysis place no stochastic constraint on the parameters.…”

Section: Related Workmentioning

confidence: 99%

“…[23] proposes a symmetric block model X ≈ C 1 AC T 2 , where C 1 ∈ {0, 1} n1×k and C 2 ∈ {0, 1} n2×k are cluster indicator matrices, and A ∈ R k×k contains the predicted output for each combination of row and column clusters. Early work on this model uses a spectral relaxation specific to squared loss [23], while later generalizations to regular exponential families [25] use EM. An equivalent formulation in terms of regular Bregman divergences [24] uses iterative majorization [22,36] as the inner loop of alternating projection.…”

Section: Related Workmentioning

confidence: 99%

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Relational learning via collective matrix factorization

Singh

Gordon

2008

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

852

280

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Relational learning is concerned with predicting unknown values of a relation, given a database of entities and observed relations among entities. An example of relational learning is movie rating prediction, where entities could include users, movies, genres, and actors. Relations would then encode users' ratings of movies, movies' genres, and actors' roles in movies. A common prediction technique given one pairwise relation, for example a #users × #movies ratings matrix, is low-rank matrix factorization. In domains with multiple relations, represented as multiple matrices, we may improve predictive accuracy by exploiting information from one relation while predicting another. To this end, we propose a collective matrix factorization model: we simultaneously factor several matrices, sharing parameters among factors when an entity participates in multiple relations. Each relation can have a different value type and error distribution; so, we allow nonlinear relationships between the parameters and outputs, using Bregman divergences to measure error. We extend standard alternating projection algorithms to our model, and derive an efficient Newton update for the projection. Furthermore, we propose stochastic optimization methods to deal with large, sparse matrices. Our model generalizes several existing matrix factorization methods, and therefore yields new large-scale optimization algorithms for these problems. Our model can handle any pairwise relational schema and a wide variety of error models. We demonstrate its efficiency, as well as the benefit of sharing parameters among relations.

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“…al. [23,24,25], and Bregman tensor clustering [5] (which can handle higher arity relations). Matrix analogues of factor analysis place no stochastic constraint on the parameters.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Relational learning via collective matrix factorization

Singh

Gordon

2008

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

852

280

View full text Add to dashboard Cite

show abstract

“…A partitional clustering algorithm like k-means is then employed to obtain the final clustering computed on the transformed spaces. In [24], a parametric probabilistic approach to cluster relational data is proposed. A Monte Carlo simulation method is used to learn the parameters and to assign objects to clusters.…”

Section: Related Workmentioning

confidence: 99%

Parameter-less co-clustering for star-structured heterogeneous data

Ienco

Robardet

Pensa

et al. 2012

Data Min Knowl Disc

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The availability of data represented with multiple features coming from heterogeneous domains is getting more and more common in real world applications. Such data represent objects of a certain type, connected to other types of data, the features, so that the overall data schema forms a star structure of inter-relationships. Co-clustering these data involves the specification of many parameters, such as the number of clusters for the object dimension and for all the features domains. In this paper we present a novel co-clustering algorithm for heterogeneous star-structured data that is parameter-less. This means that it does not require either the number of row clusters or the number of column clusters for the given feature spaces. Our approach optimizes the Goodman-Kruskal's τ , a measure for cross-association in contingency tables that evaluates the strength of the relationship between two categorical variables. We extend τ to evaluate co-clustering solutions and in particular we apply it in a higher dimensional setting. We propose the algorithm CoStar which optimizes τ by a local search approach. We assess the performance of CoStar on publicly available datasets from the textual and image domains using objective external criteria. The results show that our approach outperforms state-of-the-art methods for the co-clustering of heterogeneous data, while it remains computationally efficient.

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“…Recently, there are many clustering algorithms proposed for networks, such as spectral clustering-based methods [19,15], linkbased probabilistic models [7,1], modularity function-based algorithms [17,16], and density-based algorithms [26,25] on homogenous networks; and ranking-based algorithms [22,23], nonnegative matrix factorization [12,24], spectral clustering-based methods [13], and probabilistic approaches [14] on heterogeneous networks. However, while all these clustering methods use the information given in the networks, none considers that different users may have different purposes for clustering, nor do they ask users to help select different information for link-based clustering.…”

Section: Related Workmentioning

confidence: 99%

Integrating meta-path selection with user-guided object clustering in heterogeneous information networks

Sun

Norick

Han

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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232

192

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Real-world, multiple-typed objects are often interconnected, forming heterogeneous information networks. A major challenge for link-based clustering in such networks is its potential to generate many different results, carrying rather diverse semantic meanings. In order to generate desired clustering, we propose to use meta-path, a path that connects object types via a sequence of relations, to control clustering with distinct semantics. Nevertheless, it is easier for a user to provide a few examples ("seeds") than a weighted combination of sophisticated meta-paths to specify her clustering preference. Thus, we propose to integrate meta-path selection with user-guided clustering to cluster objects in networks, where a user first provides a small set of object seeds for each cluster as guidance. Then the system learns the weights for each meta-path that are consistent with the clustering result implied by the guidance, and generates clusters under the learned weights of meta-paths. A probabilistic approach is proposed to solve the problem, and an effective and efficient iterative algorithm, PathSelClus, is proposed to learn the model, where the clustering quality and the meta-path weights are mutually enhancing each other. Our experiments with several clustering tasks in two real networks demonstrate the power of the algorithm in comparison with the baselines.

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A probabilistic framework for relational clustering

Cited by 94 publications

References 39 publications

Relational learning via collective matrix factorization

Relational learning via collective matrix factorization

Parameter-less co-clustering for star-structured heterogeneous data

Integrating meta-path selection with user-guided object clustering in heterogeneous information networks

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