Semi-supervised Clustering on Heterogeneous Information Networks

Luo, Cheng; Pang, Wei; Wang, Zhe

doi:10.1007/978-3-319-06605-9_45

Cited by 31 publications

(12 citation statements)

References 17 publications

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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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“…Sun et al [15] present a semi-supervised clustering algorithm to generate different clustering results with path selection according to user guidance. Luo et al [79] firstly introduce the concept of relation-path to measure the similarity between same-typed objects and use the labeled information to weight relation-paths, and then propose SemiRPClus for semi-supervised learning in HIN.…”

Section: B Clusteringmentioning

confidence: 99%

A survey of heterogeneous information network analysis

Shi

Zhang

et al. 2017

IEEE Trans. Knowl. Data Eng.

899

457

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Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous networks, without distinguishing different types of objects and links in the networks. Recently, more and more researchers begin to consider these interconnected, multi-typed data as heterogeneous information networks, and develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Compared to widely studied homogeneous network, the heterogeneous information network contains richer structure and semantic information, which provides plenty of opportunities as well as a lot of challenges for data mining. In this paper, we provide a survey of heterogeneous information network analysis. We will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions. Index Termsheterogeneous information network, data mining, semi-structural data, meta path

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“…The sub dataset contains the papers published in 261 computer journals and 313 computer conferences. The schema [19] of DBLP is shown in Fig.3(a), in which Term is extracted from the titles of the papers. For the DBLP data, our recommendation problem becomes to recommend conferences to authors.…”

Section: A Datasetsmentioning

confidence: 99%

“…The works proposed by them [7][8][9][10][11][12]18] also demonstrated that by mining heterogeneous information network, one can obtain more meaningful results. It also attracted other researchers to investigate on mining the heterogeneous information network [19,23].…”

Section: B Mining Heterogeneous Social Networkmentioning

confidence: 99%

Hete-CF: Social-Based Collaborative Filtering Recommendation Using Heterogeneous Relations

Luo

Pang

Wang

et al. 2014

2014 IEEE International Conference on Data Mining

Self Cite

131

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Abstract-Collaborative filtering algorithms haven been widely used in recommender systems. However, they often suffer from the data sparsity and cold start problems. With the increasing popularity of social media, these problems may be solved by using social-based recommendation. Social-based recommendation, as an emerging research area, uses social information to help mitigate the data sparsity and cold start problems, and it has been demonstrated that the social-based recommendation algorithms can efficiently improve the recommendation performance. However, few of the existing algorithms have considered using multiple types of relations within one social network. In this paper, we investigate the social-based recommendation algorithms on heterogeneous social networks and proposed Hete-CF, a Social Collaborative Filtering algorithm using heterogeneous relations. Distinct from the exiting methods, Hete-CF can effectively utilize multiple types of relations in a heterogeneous social network. In addition, Hete-CF is a general approach and can be used in arbitrary social networks, including event based social networks, location based social networks, and any other types of heterogeneous information networks associated with social information. The experimental results on two realworld data sets, DBLP (a typical heterogeneous information network) and Meetup (a typical event based social network) show the effectiveness and efficiency of our algorithm.

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Semi-supervised Clustering on Heterogeneous Information Networks

Cited by 31 publications

References 17 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

A survey of heterogeneous information network analysis

Hete-CF: Social-Based Collaborative Filtering Recommendation Using Heterogeneous Relations

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