Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Clustering Algorithms
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Proceedings of the 13th international conference on World Wide Web
On Learning Asymmetric Dissimilarity Measures
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Automatic learning of domain model for personalized hypermedia applications
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Graph-Based Ontology Construction from Heterogenous Evidences
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Automatic taxonomy generation: issues and possibilities
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
An entropy-based hierarchical search result clustering method by utilizing augmented information
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Informative Polythetic Hierarchical Ephemeral Clustering
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Search result presentation based on faceted clustering
Proceedings of the 21st ACM international conference on Information and knowledge management
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Clustering techniques find a collection of subsets of a data set such that the collection satisfies a criterion that is dependent on a relation defined on the data set. The underlying relation is traditionally assumed to be symmetric. However, there exist many practical scenarios where the underlying relation is asymmetric. One example of an asymmetric relation in text analysis is the inclusion relation, i.e., the inclusion of the meaning of a block of text in the meaning of another block. In this paper, we consider the general problem of clustering of asymmetrically related data and propose an algorithm to cluster such data. To demonstrate its usefulness, we consider two applications in text mining: (1) summarization of short documents, and (2) generation of a concept hierarchy from a set of documents. Our experiments show that the performance of the proposed algorithm is superior to that of more traditional algorithms.