Advanced Data Clustering Methods of Mining Web Documents

Sambasivam, Samuel; Theodosopoulos, Nick

doi:10.28945/916

Cited by 25 publications

(6 citation statements)

References 3 publications

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“…In contrast to [19], we do not cluster the content with the commonly used k-means approach but rather using Suffix Tree Clustering (STC) [21], an approach that focuses on the problem of cluster labeling. We justify our choice since this clustering technique merges base clusters with high textual overlaps and was shown to outperform group average agglomerative hierarchical clustering, k-means, buckshot, fractionation and single-pass algorithms [21,14].…”

Section: Methodsmentioning

confidence: 99%

High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Review

Lacic¹,

Kowald²,

Lex³

2016

Preprint

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Air travel is one of the most frequently used means of transportation in our every-day life. Thus, it is not surprising that an increasing number of travelers share their experiences with airlines and airports in form of online reviews on the Web. In this work, we thrive to explain and uncover the features of airline reviews that contribute most to traveler satisfaction. To that end, we examine reviews crawled from the Skytrax air travel review portal. Skytrax provides four review categories to review airports, lounges, airlines and seats. Each review category consists of several five-star ratings as well as free-text review content. In this paper, we conducted a comprehensive feature study and we find that not only five-star rating information such as airport queuing time and lounge comfort highly correlate with traveler satisfaction but also textual features in the form of the inferred review text sentiment. Based on our findings, we created classifiers to predict traveler satisfaction using the best performing rating features. Our results reveal that given our methodology, traveler satisfaction can be predicted with high accuracy. Additionally, we find that training a model on the sentiment of the review text provides a competitive alternative when no five star rating information is available. We believe that our work is of interest for researchers in the area of modeling and predicting user satisfaction based on available review data on the Web.

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Section: Methodsmentioning

confidence: 99%

High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Review

Lacic¹,

Kowald²,

Lex³

2016

Preprint

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“…There are many ways to cluster web pages before finding patterns. The most common method is the K-means algorithm but there are several more like Single pass, Fractionation, Buckshot, Suffix tree and Apriori All, which are described in [Sambasivan et al 2006]. In [Sambasivan et al 2006] they also measure the execution time of the algorithms.…”

Section: Clusteringmentioning

confidence: 99%

“…The most common method is the K-means algorithm but there are several more like Single pass, Fractionation, Buckshot, Suffix tree and Apriori All, which are described in [Sambasivan et al 2006]. In [Sambasivan et al 2006] they also measure the execution time of the algorithms. Common ways to gain attributes from web pages are to take specific keywords and comparing their relevance to the rest of the text or excerpts of the web page.…”

Section: Clusteringmentioning

confidence: 99%

Web Mining

2018

The SAGE Encyclopedia of the Internet

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This paper will look closer to different implementations on web mining and the importance of filtering out calls made from robots to get knowledge about the actual human usage of a website. This is to find patterns between different web pages and create more customized and accessible web pages to users, which in turn creates more traffic and trade to the website. We will address some common methods to find and eliminate the web usage made from robots while keeping browsing data made from human users intact. This paper will primarily focus on the field of web usage mining, which is a direct need from the growth of the World Wide Web.

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“…K-means clustering method is a typical kind of the clustering algorithm based on distance [9]. Its input X={x 1 ,x 2 ,…, x n }, and classification number is k. The output is k data type C j ，j=1,2,…,k.…”

Section: The Wavelet De-noising Threshold Generation Based On K-meansmentioning

confidence: 99%

Research on the AE Signal De-noising Based on K-Means Clustering and the Wavelet Transform

Zhang¹,

Wang²

2015

IJMUE

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The noise in the acoustic emission (AE) signal must be removed to identify the mode of AE signal accurately. The Wavelet threshold de-noising method shows some unique advantages. Based on the threshold selection risky problem, K-means clustering method was used to classify the high-frequency coefficients by the wavelet decomposition to determine the removal threshold for the wavelet coefficients corresponding to the noise, and achieve the de-noising purpose. Hard-threshold method and soft-threshold method were applied to AE signal through the wavelet threshold de-noising. The thresholds generated by K-means clustering approach and the Donoho method improved were respectively used as the threshold for the de-noising of the wavelet coefficients. The experimental results show that the method proposed is superior to the Donoho method improved in the three indicators of signal to noise ratio, root mean square error.

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Advanced Data Clustering Methods of Mining Web Documents

Cited by 25 publications

References 3 publications

High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Review

High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Review

Web Mining

Research on the AE Signal De-noising Based on K-Means Clustering and the Wavelet Transform

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