Using Incremental PLSI for Threshold-Resilient Online Event Analysis

Chou, Tzu-Chuan; Chen, Meng Chang

doi:10.1109/tkde.2007.190702

Cited by 55 publications

(41 citation statements)

References 16 publications

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“…10 of this section. This measure considers the importance of similar sources in a cluster alongside sources with the news report "contribution" [138] or measure of distinctive relevance of sources in the reward value computation.…”

Section: (M−nt)mentioning

confidence: 99%

Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach

Mukherjee

Bandyopadhyay

2017

J Big Data

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This paper proposes a reinforcement learning based message transfer model for transferring news report messages through a selected path in a trusted provenance network with the objective of maximizing the reward values based on trust or importance based and network congestion or utility based cost measures. The reward values are calculated along a dynamically defined policy path connecting start topic or event node to a goal topic or event or issue nodes for incrementally defined time windows for a given network congestion situation. A hierarchy of agents of trusted roles is used to accomplish the sub-goals associated with sub-story or subtopic in the provenance structure where an agent role may assume the semantic role of the associated subtopic. The twitted news story thread or plan of events is defined in this work from the starting topic or event node to the goal topic or event node for incrementally defined intervals of time. The graphs are clustered into subtopic and these sub-goals or sub topic nodes of a topic node at every level of granularity are associated with cluster of news reports which describe activities associated with sub-goal or sub-topic events. Such cluster of nodes may also represent drilled down sequence of sub-events describing a sub-topic or sub-goal node. The policy path in a topic or story graph model is defined by applying reinforcement learning principles on dynamically defined event models associated with evolution of topic definition observed from incrementally acquired samples of input training data spanning multiple time windows. We provide a methodology for unifying similar provenance graph models for adapting and averaging the policy path classifiers associated with individual models to produce a reduced set of unified models derived during training. A minimum set cover of classifiers is identified for the models and a clustering procedure of the models is suggested based on these classifiers. Other database clustering methods have also been suggested as alternatives for clustering these models. A collection of unified models are identified from the models identified within a cluster and the policy path classifiers associated with these models provide the story or topic descriptions destined to goal topic or event nodes characterizing these models within a cluster.

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Section: (M−nt)mentioning

confidence: 99%

Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach

Mukherjee

Bandyopadhyay

2017

J Big Data

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“…As a result, many researchers have successfully applied topic models to detect topics from text data. Chou et al [13] proposed the Incremental Probabilistic Latent Semantic Indexing (IPLSI) algorithm that captures the story line of events by maintaining the continuity of the latent semantics. He et al [5] incorporated time information into a temporal Discriminative Probabilistic Model (DPM) to strengthen the topic detection performance.…”

Section: B Topic Modelmentioning

confidence: 99%

Effective Multimodality Fusion Framework for Cross-Media Topic Detection

Chu

Zhang

et al. 2016

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Due to the prevalence of "We-Media", everybody quickly publishes and receives information in various forms anywhere and anytime through the Internet. The rich crossmedia information carried by the multi-modal data in multiple media has a wide audience, deeply reflects the social realities and brings about much greater social impact than any single media information. Therefore, automatically detecting topics from crossmedia is of great benefit for the organizations (i.e., advertising agencies, governments) that care about the social opinions. However, cross-media topic detection is challenging from following aspects: 1) the multi-modal data from different media often involve distinct characteristics; 2) topics are presented in an arbitrary manner among the noisy web data. In this paper, we propose a multi-modality fusion framework and a topic recovery approach to effectively detect topics from cross-media data. The multi-modality fusion framework flexibly incorporates the heterogeneous multi-modal data into a Multi-Modality Graph (MMG), which takes full advantage from the rich cross-media information to effectively detect topic candidates. The topic recovery (TR) approach solidly improves the entirety and purity of detected topics by: 1) merging the topic candidates that are highly relevant themes of the same real topic; 2) filtering out the less-relevant noise data in the merged topic candidates. Extensive experiments on both single-media and cross-media data sets demonstrate the promising flexibility and effectiveness of our method in detecting topics from cross media.

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“…The experimental results shows better performance compared to traditional TDT. Many dynamic topic modeling system uses the variants of PLSI and LDA online topic discovery [18][19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%

Correlated Concept based Topic Updation Model for Dynamic Corpora

Jayabharathy¹,

Kanmani²,

Sivaranjani³

2014

IJCA

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A rapid growth of documents available on the Internet, digital libraries, medical documents, news wires and other scientific document corpuses has motivated the researchers to propose many text mining techniques that help users to quickly retrieve trace and summarize the information in an effective way. Topic detection is one such technique which discovers precise, meaningful and concise labels for the formulated static document clusters. This technique helps the user to navigate and retrieve the needed information quickly and efficiently. Topic updation is the process of identifying and renewing the discovered labels whenever the document clusters are updated dynamically. This paper focuses on topic updation model based on Testor theory. The proposed work is experimented using 20newsgroup and scientific literature data set. The experimental results demonstrate that the proposed algorithm exhibit better performance, compared to the existing algorithms for topic detection.

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Using Incremental PLSI for Threshold-Resilient Online Event Analysis

Cited by 55 publications

References 16 publications

Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach

Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach

Effective Multimodality Fusion Framework for Cross-Media Topic Detection

Correlated Concept based Topic Updation Model for Dynamic Corpora

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