Using Topic Keyword Clusters for Automatic Document Clustering

Chang, Hong‐Chan; Hsu, Cheng–Che

doi:10.1093/ietisy/e88-d.8.1852

Cited by 21 publications

(10 citation statements)

References 7 publications

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“…The objective of data mining is to identify valid novel, potentially useful, and understandable correlations and patterns in existing data (Chang & Hsu, 2005). The tasks of data mining can be modeled as either Predictive or Descriptive in nature (Dunham, 2003).…”

Section: Data Mining Tasksmentioning

confidence: 99%

Mining Enrollment Data Using Descriptive and Predictive Approaches

Siraj¹,

Abdoulha²

2011

Knowledge-Oriented Applications in Data Mining

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Section: Data Mining Tasksmentioning

confidence: 99%

Mining Enrollment Data Using Descriptive and Predictive Approaches

Siraj¹,

Abdoulha²

2011

Knowledge-Oriented Applications in Data Mining

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“…Concept Clustering Knowledge Graphs contain multiple concepts interrelated through multiple semantic relations together forming a semantic cluster represented by a conceptual graph [6]. Kang, Chang & Hsu use keyword to automatic cluster document [7] [8]. Topicdriven Clustering method was proposed by Zhao & George [9].…”

Section: Related Workmentioning

confidence: 99%

Concept Extraction and Clustering for Topic Digital Library Construction

Zhang

2008

2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

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“…Based on these topics we can solve problems on cross-domain text classification Barathi (2011), understanding text clustering Chang & Hsu (2005), text recommendation, and other related text data applications. There has been an exceptional amount of research on topic-model algorithms.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Latent Semantic Mapping for Automated Topic Generation

Zhou¹,

Chen²

2016

IJNDC

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Much of information sits in an unprecedented amount of text data. Managing allocation of these large scale text data is an important problem for many areas. Topic modeling performs well in this problem. The traditional generative models (PLSA,LDA) are the state-of-the-art approaches in topic modeling and most recent research on topic generation has been focusing on improving or extending these models. However, results of traditional generative models are sensitive to the number of topics K, which must be specified manually and determines the rank of solution space for topic generation. The problem of generating topics from corpus resembles community detection in networks. Many effective algorithms can automatically detect communities from networks without a manually specified number of the communities. Inspired by these algorithms, in this paper, we propose a novel method named Hierarchical Latent Semantic Mapping (HLSM), which automatically generates topics from corpus. HLSM calculates the association between each pair of words in the latent topic space, then constructs a unipartite network of words with this association and hierarchically generates topics from this network. We apply HLSM to several document collections and the experimental comparisons against several state-of-the-art approaches demonstrate the promising performance.

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Using Topic Keyword Clusters for Automatic Document Clustering

Cited by 21 publications

References 7 publications

Mining Enrollment Data Using Descriptive and Predictive Approaches

Mining Enrollment Data Using Descriptive and Predictive Approaches

Concept Extraction and Clustering for Topic Digital Library Construction

Hierarchical Latent Semantic Mapping for Automated Topic Generation

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