Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 2.541

Clustering on heterogeneous networks

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Abstract Objects that are interrelated with each other are often represented as homogeneous networks, in which objects are of the same entity type and relationships between objects are of the same relationship type. However, heterogeneous information networks, composed of multiple types of objects and/or relationships, are ubiquitous in real life. Mining heterogeneous information networks is a new and promising field of research in data mining, and clustering is an important way to identify underlying patterns in data. Although clustering on homogeneous networks has been studied for several decades, clustering on heterogeneous networks has been explored only recently. However, some progress has already been made with respect to this theme, ranging from algorithms to various related applications. This paper presents a brief summary of current research regarding heterogeneous network clustering and addresses some promising research directions. First, it presents a formalized definition and two important aspects of heterogeneous information networks to elaborate why clustering on heterogeneous networks is of significance. Then, this review provides a concise classification of existing heterogeneous network clustering algorithms based on their methodological principles. In addition, it discusses experimental developments and applications of heterogeneous network clustering. The paper addresses several open problems and critical issues for future research. WIREs Data Mining Knowl Discov 2014, 4:213–233. doi: 10.1002/widm.1126 This article is categorized under: Algorithmic Development > Structure Discovery Technologies > Computational Intelligence Technologies > Structure Discovery and Clustering
An example of a multimode network constructed from a content‐sharing website.
[ Normal View | Magnified View ]
An example of target‐object clustering based on attribute objects.
[ Normal View | Magnified View ]
An example of the simultaneous clustering of objects of each type.
[ Normal View | Magnified View ]
An example of a multidimensional‐transformed homogeneous network.
[ Normal View | Magnified View ]
An example of a multimode‐transformed homogeneous network.
[ Normal View | Magnified View ]
A classification of heterogeneous network clustering methods according to their underlying methodological principles.
[ Normal View | Magnified View ]
An example of a multidimensional network constructed from multiple websites.
[ Normal View | Magnified View ]
An example of a multidimensional network constructed from a content‐sharing website.
[ Normal View | Magnified View ]
An example of a word‐document network.
[ Normal View | Magnified View ]

Browse by Topic

Technologies > Computational Intelligence
Technologies > Structure Discovery and Clustering
Algorithmic Development > Structure Discovery

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts