An Enhanced Similarity Measure for Utilizing Site Structure in Web Personalization Systems

Sahebi, Shaghayegh; Oroumchian, Farhad; Khosravi, Ramtin

doi:10.1109/wiiat.2008.270

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…To gain a proper result in proposed method, accept and reject ratios are increased from 0.01 to 0.99 with 0.001 step size. In this method, utilizing a point to point similarity, the best number of clusters is determined automatically [13]. The subtractive clustering algorithm is applied on the described data set.…”

Section: Evaluation and Implementation Of The Proposed Methodsmentioning

confidence: 99%

Assessment and Validating the Quality of Educational Web Sites using Subtractive Clustering

Afshoon¹,

Harounabadi²,

Abedini³

2014

IJCA

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Researchers have studied qualitative and quantitative methods to assess the quality of website. Previous studies had determined criteria such as quality of service. Human behavior, namely the objective perspective, is the essential source to obtain human thinking and real doings. For this reason, data mining approaches are used to acquire the objective source. In this research, proposed subtractive clustering is applied in evaluating educational web sites from the fuzzy objective perspective. An empirical study is carried out to validate the model capability. Results indicate that in the recommended algorithm are closer to the real data.

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Section: Evaluation and Implementation Of The Proposed Methodsmentioning

confidence: 99%

Assessment and Validating the Quality of Educational Web Sites using Subtractive Clustering

Afshoon¹,

Harounabadi²,

Abedini³

2014

IJCA

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“…The content and structure of web pages has previously been investigated from the perspective of search engine optimisation [26], data retrieval [28], website design [29], and web navigation [30].…”

Section: Related Workmentioning

confidence: 99%

Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends

Stoehr

Braesemann

Frommelt

et al. 2020

Springer Proceedings in Complexity

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The digital transformation is driving revolutionary innovations and new market entrants threaten established sectors of the economy such as the automotive industry. Following the need for monitoring shifting industries, we present a network-centred analysis of car manufacturer web pages. Solely exploiting publicly-available information, we construct large networks from web pages and hyperlinks. The network properties disclose the internal corporate positioning of the three largest automotive manufacturers, Toyota, Volkswagen and Hyundai with respect to innovative trends and their international outlook. We tag web pages concerned with topics like e-mobility & environment or autonomous driving, and investigate their relevance in the network. Sentiment analysis on individual web pages uncovers a relationship between page linking and use of positive language, particularly with respect to innovative trends. Web pages of the same country domain form clusters of different size in the network that reveal strong correlations with sales market orientation. Our approach maintains the web content's hierarchical structure imposed by the web page networks. It, thus, presents a method to reveal hierarchical structures of unstructured text content obtained from web scraping. It is highly transparent, reproducible and data driven, and could be used to gain complementary insights into innovative strategies of firms and competitive landscapes, which would not be detectable by the analysis of web content alone.

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“…In rough set based approach, each click stream transaction is initially a singleton cluster. The similarity upper approximation for each click stream transaction is calculated for a given set of transactions [10] [11]. At every step of agglomerative clustering, the clusters are agglomerated based on the similarity upper approximation.…”

Section: Background Of Studymentioning

confidence: 99%

An Efficient Agglomerative Clustering Algorithm for Web Navigation Pattern Identification

Anitha¹

2016

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Web log mining is analysis of web log files with web page sequences. Discovering user access patterns from web access are necessary for building adaptive web servers, to improve e-commerce, to carry out cross-marketing, for web personalization, to predict web access sequence etc. In this paper, a new agglomerative clustering technique is proposed to identify users with similar interest, and to determine the motivation for visiting a website. Using this approach, web usage mining is done through different stages namely data cleaning, preprocessing, pattern discovery and pattern analysis. Results are given to explain how this approach produces tight usage clusters than the existing web usage mining techniques. Rather than traditional distance based clustering, the similarity measure is considered during clustering process in order to reduce computational complexity. This paper also deals with the problem of assessing the quality of user session clusters and cluster validity is measured by using statistical test, which measures the distances of clusters distributions to infer their dissimilarity and distinguish level. Using such statistical measures, it is proved that cluster accuracy is improved to the extent of 0.83, over existing k-means clustering with validity measure 0.26, FCM (Fuzzy C Means) clustering with validity measure 0.56. Rough set based clustering with validity measure 0.54 Generation of dense clusters is essential for finding interesting patterns needed for further mining and analysis.

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An Enhanced Similarity Measure for Utilizing Site Structure in Web Personalization Systems

Cited by 8 publications

References 5 publications

Assessment and Validating the Quality of Educational Web Sites using Subtractive Clustering

Assessment and Validating the Quality of Educational Web Sites using Subtractive Clustering

Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends

An Efficient Agglomerative Clustering Algorithm for Web Navigation Pattern Identification

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