Analysis of the Generator and Consistency of General Web Page Layout Structure Using Matching Algorithm Based on Set Difference

Author(s):  
Agniya Noor Ilhamiati ◽  
Dana Sulistyo Kusumo ◽  
Indra Lukmana Sardi
2005 ◽  
Vol 11 (1) ◽  
pp. 34-44 ◽  
Author(s):  
Xing Xie ◽  
Chong Wang ◽  
Li-Qun Chen ◽  
Wei-Ying Ma

2021 ◽  
Vol 40 (7) ◽  
pp. 33-44
Author(s):  
K. Kikuchi ◽  
M. Otani ◽  
K. Yamaguchi ◽  
E. Simo‐Serra

2021 ◽  
Vol 21 (2) ◽  
pp. 105-120
Author(s):  
K. S. Sakunthala Prabha ◽  
C. Mahesh ◽  
S. P. Raja

Abstract Topic precise crawler is a special purpose web crawler, which downloads appropriate web pages analogous to a particular topic by measuring cosine similarity or semantic similarity score. The cosine based similarity measure displays inaccurate relevance score, if topic term does not directly occur in the web page. The semantic-based similarity measure provides the precise relevance score, even if the synonyms of the given topic occur in the web page. The unavailability of the topic in the ontology produces inaccurate relevance score by the semantic focused crawlers. This paper overcomes these glitches with a hybrid string-matching algorithm by combining the semantic similarity-based measure with the probabilistic similarity-based measure. The experimental results revealed that this algorithm increased the efficiency of the focused web crawlers and achieved better Harvest Rate (HR), Precision (P) and Irrelevance Ratio (IR) than the existing web focused crawlers achieve.


2016 ◽  
Vol 51 (10) ◽  
pp. 181-194 ◽  
Author(s):  
Pavel Panchekha ◽  
Emina Torlak

Author(s):  
Sathiyamoorthi V

It is generally observed throughout the world that in the last two decades, while the average speed of computers has almost doubled in a span of around eighteen months, the average speed of the network has doubled merely in a span of just eight months! In order to improve the performance, more and more researchers are focusing their research in the field of computers and its related technologies. Data Mining is also known as knowledge discovery in database (KDD) is one such research area. The discovered knowledge can be applied in various application areas such as marketing, fraud detection, customer retention and production control and marketing to improve their business. It discovers implicit, previously unknown and potentially useful information out of datasets. Recent trends in data mining include web mining where it discovers knowledge from web based information to improve the page layout, structure and its content thereby it reduces the user latency in accessing the web page and website performance.


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