Model-based clustering of short text streams

Jianhua Yin; Wei Zhang; Daren Chao; Xiaohui Yu; Zhongkun Liu; Jianyong Wang

doi:10.1145/3219819.3220094

Model-based clustering of short text streams

Jianhua Yin
, Wei Zhang^*
, Daren Chao
, Xiaohui Yu
, Zhongkun Liu
, Jianyong Wang

^*Corresponding author for this work

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

68 Scopus citations

Abstract

Short text stream clustering has become an increasingly important problem due to the explosive growth of short text in diverse social medias. In this paper, we propose a model-based short text stream clustering algorithm (MStream) which can deal with the concept drift problem and sparsity problem naturally. The MStream algorithm can achieve state-of-the-art performance with only one pass of the stream, and can have even better performance when we allow multiple iterations of each batch. We further propose an improved algorithm of MStream with forgetting rules called MStreamF, which can efficiently delete outdated documents by deleting clusters of outdated batches. Our extensive experimental study shows that MStream and MStreamF can achieve better performance than three baselines on several real datasets.

Original language	English
Title of host publication	KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	2634-2642
Number of pages	9
ISBN (Print)	9781450355520
DOIs	https://doi.org/10.1145/3219819.3220094
State	Published - 19 Jul 2018
Event	24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 - London, United Kingdom Duration: 19 Aug 2018 → 23 Aug 2018

Publication series

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

Conference

Conference	24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
Country/Territory	United Kingdom
City	London
Period	19/08/18 → 23/08/18

Keywords

Dirichlet Process
Mixture Model
Text Stream Clustering

Access to Document

10.1145/3219819.3220094

Cite this

Yin, J., Zhang, W., Chao, D., Yu, X., Liu, Z., & Wang, J. (2018). Model-based clustering of short text streams. In KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 2634-2642). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3219819.3220094

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title = "Model-based clustering of short text streams",

abstract = "Short text stream clustering has become an increasingly important problem due to the explosive growth of short text in diverse social medias. In this paper, we propose a model-based short text stream clustering algorithm (MStream) which can deal with the concept drift problem and sparsity problem naturally. The MStream algorithm can achieve state-of-the-art performance with only one pass of the stream, and can have even better performance when we allow multiple iterations of each batch. We further propose an improved algorithm of MStream with forgetting rules called MStreamF, which can efficiently delete outdated documents by deleting clusters of outdated batches. Our extensive experimental study shows that MStream and MStreamF can achieve better performance than three baselines on several real datasets.",

keywords = "Dirichlet Process, Mixture Model, Text Stream Clustering",

author = "Jianhua Yin and Wei Zhang and Daren Chao and Xiaohui Yu and Zhongkun Liu and Jianyong Wang",

note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 ; Conference date: 19-08-2018 Through 23-08-2018",

year = "2018",

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series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

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Yin, J, Zhang, W, Chao, D, Yu, X, Liu, Z & Wang, J 2018, Model-based clustering of short text streams. in KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 2634-2642, 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018, London, United Kingdom, 19/08/18. https://doi.org/10.1145/3219819.3220094

Model-based clustering of short text streams. / Yin, Jianhua; Zhang, Wei; Chao, Daren et al.
KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2018. p. 2634-2642 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Model-based clustering of short text streams

AU - Yin, Jianhua

AU - Zhang, Wei

AU - Chao, Daren

AU - Yu, Xiaohui

AU - Liu, Zhongkun

AU - Wang, Jianyong

PY - 2018/7/19

Y1 - 2018/7/19

N2 - Short text stream clustering has become an increasingly important problem due to the explosive growth of short text in diverse social medias. In this paper, we propose a model-based short text stream clustering algorithm (MStream) which can deal with the concept drift problem and sparsity problem naturally. The MStream algorithm can achieve state-of-the-art performance with only one pass of the stream, and can have even better performance when we allow multiple iterations of each batch. We further propose an improved algorithm of MStream with forgetting rules called MStreamF, which can efficiently delete outdated documents by deleting clusters of outdated batches. Our extensive experimental study shows that MStream and MStreamF can achieve better performance than three baselines on several real datasets.

AB - Short text stream clustering has become an increasingly important problem due to the explosive growth of short text in diverse social medias. In this paper, we propose a model-based short text stream clustering algorithm (MStream) which can deal with the concept drift problem and sparsity problem naturally. The MStream algorithm can achieve state-of-the-art performance with only one pass of the stream, and can have even better performance when we allow multiple iterations of each batch. We further propose an improved algorithm of MStream with forgetting rules called MStreamF, which can efficiently delete outdated documents by deleting clusters of outdated batches. Our extensive experimental study shows that MStream and MStreamF can achieve better performance than three baselines on several real datasets.

KW - Dirichlet Process

KW - Mixture Model

KW - Text Stream Clustering

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DO - 10.1145/3219819.3220094

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AN - SCOPUS:85051473763

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T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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BT - KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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ER -

Yin J, Zhang W, Chao D, Yu X, Liu Z, Wang J. Model-based clustering of short text streams. In KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2018. p. 2634-2642. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3219819.3220094

Model-based clustering of short text streams

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Keywords

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