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However, the clustering of such networks can be challenging since (a) the attribute values of objects are often incomplete, which implies that an object may carry only partial attributes or even no attributes to correctly label itself; and (b) the links of different types may carry different kinds of semantic meanings, and it is a difficult task to determine the nature of their relative importance in helping the clustering for a given purpose. In this paper, we address these challenges by proposing a model-based clustering algorithm. We design a probabilistic model which clusters the objects of different types into a common hidden space, by using a user-specified set of attributes, as well as the links from different relations. The strengths of different types of links are automatically learned, and are determined by the given purpose of clustering. An iterative algorithm is designed for solving the clustering problem, in which the strengths of different types of links and the quality of clustering results mutually enhance each other. Our experimental results on real and synthetic data sets demonstrate the effectiveness and efficiency of the algorithm.<\/jats:p>","DOI":"10.14778\/2140436.2140437","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"394-405","source":"Crossref","is-referenced-by-count":108,"title":["Relation strength-aware clustering of heterogeneous information networks with incomplete attributes"],"prefix":"10.14778","volume":"5","author":[{"given":"Yizhou","family":"Sun","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL"}]},{"given":"Charu C.","family":"Aggarwal","sequence":"additional","affiliation":[{"name":"IBM T. J. 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