“An Inclusive Survey on Data Preprocessing Methods Used in Web Usage Mining”

Bakariya, Brijesh; Mohbey, Krishna Kumar; Thakur, Ghanshyam Singh

doi:10.1007/978-81-322-1041-2_35

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…Before applying data mining technique for server log [12,21,31], it needs to preprocess that server log and after applying preprocessing technique they will transform in structure form and split according to its attribute. In Table 1, we are showing a sample of the preprocessed weblog.…”

Section: Finding Utility Of Transactional Datasetmentioning

confidence: 99%

“…Utility itemset mining is an important field of data mining; its aim to extract high utility itemset from databases and those itemsets, which have high project, are founded [11]. Here, the utility of itemset means profitability importance and interestingness of the item [12,13]. In a transactional database, the utility of an item depends on two types of utility: internal utility and external utility as we are shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this problem, frequent pattern growth mining algorithm has been used [3,23]. Here a data structure is used to store data which is called frequent pattern tree [12,24]. In this algorithm, frequent pattern can be found out only after scanning the database twice.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Algorithm for Extracting High Utility Itemsets from Weblog Data

Bakariya

Thakur

2015

IETE Technical Review

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High utility itemset refers to those set of items which has high utility such as profit in a database. High utility of itemset plays a crucial role in real life. In recent years, various algorithms have been proposed for finding high utility itemset but unfortunately they are not completely relevant at the time and space point of view. In the data mining field, high utility itemset can be found in different categories of data like time series, categorical, etc. Log data is useful for finding behaviour of the user in different aspects. In this paper, we have proposed an algorithm named HUIM (High Utility Itemsets Mining) and construct HUI-FP (High Utility Itemsets-Frequent Pattern) Tree for efficiently mining high utility itemsets from log database. The behaviour of the user can be predicted through the high utility of every visited page. We have also proposed pattern generation technique based on cosine similarities among itemsets. These techniques generate strong patterns, and customized users profile according to that pattern. The proposed algorithm is better than the previous state of the art algorithm for high utility itemset generation.

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Section: Finding Utility Of Transactional Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient Algorithm for Extracting High Utility Itemsets from Weblog Data

Bakariya

Thakur

2015

IETE Technical Review

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“…Preprocessing techniques based on some heuristics is proposed in [6]. [7] and [8] A sample HTTP request for the NASA Kennedy Space Centre WWW server [10] is depicted in figure 1. The web access log line in Figure 1 shows that a user from the IP address 199.72.81.55 successfully requested the page "apollo" on 01 st July 1995 at 00:00:01 a.m.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Discovery from Web Usage Data: An Efficient Implementation of Web Log Preprocessing Techniques

Shivaprasad¹,

Reddy²,

Acharya³

2015

IJCA

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“…When the users visit any web site, their all activities are appended into web log files. Even a single click, including images, html document or other objects get logged in web log files [1]. The information in Log files includes User Name, Visiting path, Path traversed, Time Stamp, Page Last Visited, User Agent, URL, and Request type [2].…”

Section: Introductionmentioning

confidence: 99%

Prevention of DDoS and Brute Force Attacks on Web Log Files using Combination of Genetic Algorithm and Feed forward Back Propagation Neural Network

Kaur¹,

Singh²,

Kaur³

2015

IJCA

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World Wide Web has become an ultimate source of information. Traditional services such as banking, education, medicine, defence, and transportation are being presented by web applications. Whenever the users make use of any web application, all the activities of the users get automatically get appended into the web log files. The web log file data helps the website owners in number of ways such as customization of web content, pre-fetching and caching, E-commerce, etc. However, the web log file data is exposed to number of attacks. In this paper, a new technique has been proposed to prevent the log file data from the two most common attacks: Brute force attack and DDoS attack.

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“An Inclusive Survey on Data Preprocessing Methods Used in Web Usage Mining”

Cited by 7 publications

References 13 publications

An Efficient Algorithm for Extracting High Utility Itemsets from Weblog Data

An Efficient Algorithm for Extracting High Utility Itemsets from Weblog Data

Knowledge Discovery from Web Usage Data: An Efficient Implementation of Web Log Preprocessing Techniques

Prevention of DDoS and Brute Force Attacks on Web Log Files using Combination of Genetic Algorithm and Feed forward Back Propagation Neural Network

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