Automatic Pattern-Taxonomy Extraction for Web Mining

Wu, Sheng‐Tang; Li, Yuefeng; Xu, Yue; Pham, Binh; Chen, Yi‐Ping Phoebe

doi:10.1109/wi.2004.10132

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…As is the case with other unsupervised methods, the specific technique depends on the application. We note that mining patterns from transactional data has been successfully used in many areas, such as analysis of retail transaction data [23], biomedical data analysis [19,24] and information retrieval [25]. The approach of finding patterns based on compression and small description have been found to be useful in many settings [22,[26][27][28].…”

Section: Principal Findings and Previous Workmentioning

confidence: 99%

An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study (Preprint)

Sambaturu¹,

Bhattacharya²,

Chen³

et al. 2018

Preprint

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BACKGROUND Agencies such as the Centers for Disease Control and Prevention (CDC) currently release incidence data (e.g., Influenza), along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data. OBJECTIVE Our goal is to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as: regions which have specific characteristics, e.g., high incidence in a particular week, those which showed a sudden change in incidence, or regions which have significantly different incidence compared to earlier seasons. METHODS We develop techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involves using the principle of minimum description length (MDL) for representing a given target set in terms of combinations of attributes (referred to as clauses); we consider both positive and negative clauses, and relaxed descriptions, which approximately represent the set, and use integer programming to find such descriptions. Finally, we design an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as their compression ratio). RESULTS We apply our methods for finding spatio-temporal patterns in the spread of seasonal Influenza in the United States (US) using state level ILI activity indicator data from the CDC. We observe that the compression ratios are over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses are allowed. Sets with high compression ratios (e.g., over 2.5) correspond to interesting patterns in the spatio-temporal dynamics of ILI. Our approach also outperforms other baselines in terms of the compression ratio. CONCLUSIONS Our approach, which is an unsupervised machine learning method, can provide new insights into the patterns and trends in disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions, beyond Influenza in the US. Our approach can be easily adapted for automated generation of narratives. CLINICALTRIAL

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Section: Principal Findings and Previous Workmentioning

confidence: 99%

An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study (Preprint)

Sambaturu¹,

Bhattacharya²,

Chen³

et al. 2018

Preprint

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“…There has been prior work in using association rules to find interesting patterns within text documents. Association rule mining has been used for text mining and web text mining for the purpose of pattern, trend, event discovery and text classification (Feldman & Hirsh, 1996;Holt & Chung, 2001;Wu et al, 2004). In this line of research, the associations between terms and categories were described using association rules.…”

Section: Prior Work In Text Mining Using Association Rulesmentioning

confidence: 99%

Automatic Item Weight Generation for Pattern Mining and its Application

Pears

Koh

Dobbie

2011

International Journal of Data Warehousing and Mining

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Association rule mining discovers relationships among items in a transactional database. Most approaches assume that all items within a dataset have a uniform distribution with respect to support. However, this is not always the case, and weighted association rule mining (WARM) was introduced to provide importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. In certain cases, it is difficult to provide weights to all items within a dataset. In this paper, the authors propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. The authors experiment shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared with a miner that does not employ a weighting scheme. The authors applied the model in a real world application to mine text from a given collection of documents. The use of item weighting enabled the authors to attach more importance to terms that are distinctive. The results demonstrate that keyword discrimination via item weighting leads to informative rules.

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Utilizing Search Intent in Topic Ontology-Based User Profile for Web Mining

Zhou

et al. 2006

2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06)

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Automatic Pattern-Taxonomy Extraction for Web Mining

Abstract: Abstract

Cited by 7 publications

References 19 publications

An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study (Preprint)

An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study (Preprint)

Automatic Item Weight Generation for Pattern Mining and its Application

Utilizing Search Intent in Topic Ontology-Based User Profile for Web Mining

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