A Dirichlet process biterm-based mixture model for short text stream clustering
Abstract Short text stream clustering has become an important problem for mining textual data in diverse social media platforms (e.g., Twitter). However, most of the existing clustering methods (e.g., LDA and PLSA) are developed based on the assumption of a static corpus of long texts, while little...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Junyang [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 50(2020), 5 vom: 01. Feb., Seite 1609-1619 |
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| Übergeordnetes Werk: |
volume:50 ; year:2020 ; number:5 ; day:01 ; month:02 ; pages:1609-1619 |
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| DOI / URN: |
10.1007/s10489-019-01606-1 |
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OLC2066109754 |
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| 520 | |a Abstract Short text stream clustering has become an important problem for mining textual data in diverse social media platforms (e.g., Twitter). However, most of the existing clustering methods (e.g., LDA and PLSA) are developed based on the assumption of a static corpus of long texts, while little attention has been given to short text streams. Different from the long texts, the clustering of short texts is more challenging since their word co-occurrence pattern easily suffers from a sparsity problem. In this paper, we propose a Dirichlet process biterm-based mixture model (DP-BMM), which can deal with the topic drift problem and the sparsity problem in short text stream clustering. The major advantages of DP-BMM include (1) DP-BMM explicitly exploits the word-pairs constructed from each document to enhance the word co-occurrence pattern in short texts; (2) DP-BMM can deal with the topic drift problem of short text streams naturally. Moreover, we further propose an improved algorithm of DP-BMM with forgetting property called DP-BMM-FP, which can efficiently delete biterms of outdated documents by deleting clusters of outdated batches. To perform inference, we adopt an online Gibbs sampling method for parameter estimation. Our extensive experimental results on real-world datasets show that DP-BMM and DP-BMM-FP can achieve a better performance than the state-of-the-art methods in terms of NMI metrics. | ||
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| 650 | 4 | |a Stream clustering | |
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| 700 | 1 | |a Gong, Zhiguo |4 aut | |
| 700 | 1 | |a Liu, Weiwen |4 aut | |
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10.1007/s10489-019-01606-1 doi (DE-627)OLC2066109754 (DE-He213)s10489-019-01606-1-p DE-627 ger DE-627 rakwb eng 004 VZ Chen, Junyang verfasserin (orcid)0000-0002-1139-8654 aut A Dirichlet process biterm-based mixture model for short text stream clustering 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Short text stream clustering has become an important problem for mining textual data in diverse social media platforms (e.g., Twitter). However, most of the existing clustering methods (e.g., LDA and PLSA) are developed based on the assumption of a static corpus of long texts, while little attention has been given to short text streams. Different from the long texts, the clustering of short texts is more challenging since their word co-occurrence pattern easily suffers from a sparsity problem. In this paper, we propose a Dirichlet process biterm-based mixture model (DP-BMM), which can deal with the topic drift problem and the sparsity problem in short text stream clustering. The major advantages of DP-BMM include (1) DP-BMM explicitly exploits the word-pairs constructed from each document to enhance the word co-occurrence pattern in short texts; (2) DP-BMM can deal with the topic drift problem of short text streams naturally. Moreover, we further propose an improved algorithm of DP-BMM with forgetting property called DP-BMM-FP, which can efficiently delete biterms of outdated documents by deleting clusters of outdated batches. To perform inference, we adopt an online Gibbs sampling method for parameter estimation. Our extensive experimental results on real-world datasets show that DP-BMM and DP-BMM-FP can achieve a better performance than the state-of-the-art methods in terms of NMI metrics. Data mining Stream clustering Topic modeling Gong, Zhiguo aut Liu, Weiwen aut Enthalten in Applied intelligence Springer US, 1991 50(2020), 5 vom: 01. Feb., Seite 1609-1619 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:50 year:2020 number:5 day:01 month:02 pages:1609-1619 https://doi.org/10.1007/s10489-019-01606-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 50 2020 5 01 02 1609-1619 |
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10.1007/s10489-019-01606-1 doi (DE-627)OLC2066109754 (DE-He213)s10489-019-01606-1-p DE-627 ger DE-627 rakwb eng 004 VZ Chen, Junyang verfasserin (orcid)0000-0002-1139-8654 aut A Dirichlet process biterm-based mixture model for short text stream clustering 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Short text stream clustering has become an important problem for mining textual data in diverse social media platforms (e.g., Twitter). However, most of the existing clustering methods (e.g., LDA and PLSA) are developed based on the assumption of a static corpus of long texts, while little attention has been given to short text streams. Different from the long texts, the clustering of short texts is more challenging since their word co-occurrence pattern easily suffers from a sparsity problem. In this paper, we propose a Dirichlet process biterm-based mixture model (DP-BMM), which can deal with the topic drift problem and the sparsity problem in short text stream clustering. The major advantages of DP-BMM include (1) DP-BMM explicitly exploits the word-pairs constructed from each document to enhance the word co-occurrence pattern in short texts; (2) DP-BMM can deal with the topic drift problem of short text streams naturally. Moreover, we further propose an improved algorithm of DP-BMM with forgetting property called DP-BMM-FP, which can efficiently delete biterms of outdated documents by deleting clusters of outdated batches. To perform inference, we adopt an online Gibbs sampling method for parameter estimation. Our extensive experimental results on real-world datasets show that DP-BMM and DP-BMM-FP can achieve a better performance than the state-of-the-art methods in terms of NMI metrics. Data mining Stream clustering Topic modeling Gong, Zhiguo aut Liu, Weiwen aut Enthalten in Applied intelligence Springer US, 1991 50(2020), 5 vom: 01. Feb., Seite 1609-1619 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:50 year:2020 number:5 day:01 month:02 pages:1609-1619 https://doi.org/10.1007/s10489-019-01606-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 50 2020 5 01 02 1609-1619 |
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10.1007/s10489-019-01606-1 doi (DE-627)OLC2066109754 (DE-He213)s10489-019-01606-1-p DE-627 ger DE-627 rakwb eng 004 VZ Chen, Junyang verfasserin (orcid)0000-0002-1139-8654 aut A Dirichlet process biterm-based mixture model for short text stream clustering 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Short text stream clustering has become an important problem for mining textual data in diverse social media platforms (e.g., Twitter). However, most of the existing clustering methods (e.g., LDA and PLSA) are developed based on the assumption of a static corpus of long texts, while little attention has been given to short text streams. Different from the long texts, the clustering of short texts is more challenging since their word co-occurrence pattern easily suffers from a sparsity problem. In this paper, we propose a Dirichlet process biterm-based mixture model (DP-BMM), which can deal with the topic drift problem and the sparsity problem in short text stream clustering. The major advantages of DP-BMM include (1) DP-BMM explicitly exploits the word-pairs constructed from each document to enhance the word co-occurrence pattern in short texts; (2) DP-BMM can deal with the topic drift problem of short text streams naturally. Moreover, we further propose an improved algorithm of DP-BMM with forgetting property called DP-BMM-FP, which can efficiently delete biterms of outdated documents by deleting clusters of outdated batches. To perform inference, we adopt an online Gibbs sampling method for parameter estimation. Our extensive experimental results on real-world datasets show that DP-BMM and DP-BMM-FP can achieve a better performance than the state-of-the-art methods in terms of NMI metrics. Data mining Stream clustering Topic modeling Gong, Zhiguo aut Liu, Weiwen aut Enthalten in Applied intelligence Springer US, 1991 50(2020), 5 vom: 01. Feb., Seite 1609-1619 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:50 year:2020 number:5 day:01 month:02 pages:1609-1619 https://doi.org/10.1007/s10489-019-01606-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 50 2020 5 01 02 1609-1619 |
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10.1007/s10489-019-01606-1 doi (DE-627)OLC2066109754 (DE-He213)s10489-019-01606-1-p DE-627 ger DE-627 rakwb eng 004 VZ Chen, Junyang verfasserin (orcid)0000-0002-1139-8654 aut A Dirichlet process biterm-based mixture model for short text stream clustering 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Short text stream clustering has become an important problem for mining textual data in diverse social media platforms (e.g., Twitter). However, most of the existing clustering methods (e.g., LDA and PLSA) are developed based on the assumption of a static corpus of long texts, while little attention has been given to short text streams. Different from the long texts, the clustering of short texts is more challenging since their word co-occurrence pattern easily suffers from a sparsity problem. In this paper, we propose a Dirichlet process biterm-based mixture model (DP-BMM), which can deal with the topic drift problem and the sparsity problem in short text stream clustering. The major advantages of DP-BMM include (1) DP-BMM explicitly exploits the word-pairs constructed from each document to enhance the word co-occurrence pattern in short texts; (2) DP-BMM can deal with the topic drift problem of short text streams naturally. Moreover, we further propose an improved algorithm of DP-BMM with forgetting property called DP-BMM-FP, which can efficiently delete biterms of outdated documents by deleting clusters of outdated batches. To perform inference, we adopt an online Gibbs sampling method for parameter estimation. Our extensive experimental results on real-world datasets show that DP-BMM and DP-BMM-FP can achieve a better performance than the state-of-the-art methods in terms of NMI metrics. Data mining Stream clustering Topic modeling Gong, Zhiguo aut Liu, Weiwen aut Enthalten in Applied intelligence Springer US, 1991 50(2020), 5 vom: 01. Feb., Seite 1609-1619 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:50 year:2020 number:5 day:01 month:02 pages:1609-1619 https://doi.org/10.1007/s10489-019-01606-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 50 2020 5 01 02 1609-1619 |
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Abstract Short text stream clustering has become an important problem for mining textual data in diverse social media platforms (e.g., Twitter). However, most of the existing clustering methods (e.g., LDA and PLSA) are developed based on the assumption of a static corpus of long texts, while little attention has been given to short text streams. Different from the long texts, the clustering of short texts is more challenging since their word co-occurrence pattern easily suffers from a sparsity problem. In this paper, we propose a Dirichlet process biterm-based mixture model (DP-BMM), which can deal with the topic drift problem and the sparsity problem in short text stream clustering. The major advantages of DP-BMM include (1) DP-BMM explicitly exploits the word-pairs constructed from each document to enhance the word co-occurrence pattern in short texts; (2) DP-BMM can deal with the topic drift problem of short text streams naturally. Moreover, we further propose an improved algorithm of DP-BMM with forgetting property called DP-BMM-FP, which can efficiently delete biterms of outdated documents by deleting clusters of outdated batches. To perform inference, we adopt an online Gibbs sampling method for parameter estimation. Our extensive experimental results on real-world datasets show that DP-BMM and DP-BMM-FP can achieve a better performance than the state-of-the-art methods in terms of NMI metrics. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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Abstract Short text stream clustering has become an important problem for mining textual data in diverse social media platforms (e.g., Twitter). However, most of the existing clustering methods (e.g., LDA and PLSA) are developed based on the assumption of a static corpus of long texts, while little attention has been given to short text streams. Different from the long texts, the clustering of short texts is more challenging since their word co-occurrence pattern easily suffers from a sparsity problem. In this paper, we propose a Dirichlet process biterm-based mixture model (DP-BMM), which can deal with the topic drift problem and the sparsity problem in short text stream clustering. The major advantages of DP-BMM include (1) DP-BMM explicitly exploits the word-pairs constructed from each document to enhance the word co-occurrence pattern in short texts; (2) DP-BMM can deal with the topic drift problem of short text streams naturally. Moreover, we further propose an improved algorithm of DP-BMM with forgetting property called DP-BMM-FP, which can efficiently delete biterms of outdated documents by deleting clusters of outdated batches. To perform inference, we adopt an online Gibbs sampling method for parameter estimation. Our extensive experimental results on real-world datasets show that DP-BMM and DP-BMM-FP can achieve a better performance than the state-of-the-art methods in terms of NMI metrics. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract Short text stream clustering has become an important problem for mining textual data in diverse social media platforms (e.g., Twitter). However, most of the existing clustering methods (e.g., LDA and PLSA) are developed based on the assumption of a static corpus of long texts, while little attention has been given to short text streams. Different from the long texts, the clustering of short texts is more challenging since their word co-occurrence pattern easily suffers from a sparsity problem. In this paper, we propose a Dirichlet process biterm-based mixture model (DP-BMM), which can deal with the topic drift problem and the sparsity problem in short text stream clustering. The major advantages of DP-BMM include (1) DP-BMM explicitly exploits the word-pairs constructed from each document to enhance the word co-occurrence pattern in short texts; (2) DP-BMM can deal with the topic drift problem of short text streams naturally. Moreover, we further propose an improved algorithm of DP-BMM with forgetting property called DP-BMM-FP, which can efficiently delete biterms of outdated documents by deleting clusters of outdated batches. To perform inference, we adopt an online Gibbs sampling method for parameter estimation. Our extensive experimental results on real-world datasets show that DP-BMM and DP-BMM-FP can achieve a better performance than the state-of-the-art methods in terms of NMI metrics. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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A Dirichlet process biterm-based mixture model for short text stream clustering |
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Gong, Zhiguo Liu, Weiwen |
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