A survey of fuzzy web mining

Lin, Chun–Wei; Hong, Tzung-Pei

doi:10.1002/widm.1091

Cited by 37 publications

(14 citation statements)

References 67 publications

(65 reference statements)

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“…They employed iterative rule learning to have knowhow on the user behavior. A good survey of fuzzy web mining can be found in [23] where techniques pertaining to fuzzy web structure mining, fuzzy web content mining and fuzzy web usage mining. Velesquez [24] combined both web usage mining and eye-tracking technologies for classifying web site key objects.…”

Section: Related Workmentioning

confidence: 99%

An Extensible Web Mining Framework for Real Knowledge

Pushpalatha¹,

Chandra²

2017

IJEACS

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Abstract-With the emergence of Web 2.0 applications that bestow rich user experience and convenience without time and geographical restrictions, web usage logs became a goldmine to researchers across the globe. User behavior analysis in different domains based on web logs has its utility for enterprises to have strategic decision making. Business growth of enterprises depends on customer-centric approaches that need to know the knowledge of customer behavior to succeed. The rationale behind this is that customers have alternatives and there is intense competition. Therefore business community needs business intelligence to have expert decisions besides focusing customer relationship management. Many researchers contributed towards this end. However, the need for a comprehensive framework that caters to the needs of businesses to ascertain real needs of web users. This paper presents a framework named eXtensible Web Usage Mining Framework (XWUMF) for discovering actionable knowledge from web log data. The framework employs a hybrid approach that exploits fuzzy clustering methods and methods for user behavior analysis. Moreover the framework is extensible as it can accommodate new algorithms for fuzzy clustering and user behavior analysis. We proposed an algorithm known as Sequential Web Usage Miner (SWUM) for efficient mining of web usage patterns from different datasets. We built a prototype application to validate our framework. Our empirical results revealed that the framework helps in discovering actionable knowledge.

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Section: Related Workmentioning

confidence: 99%

An Extensible Web Mining Framework for Real Knowledge

Pushpalatha¹,

Chandra²

2017

IJEACS

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“…Fuzzy based high fuzzy utility webpage sets extracted by comparing minimum fuzzy utility threshold value. For example, data and knowledge on the web may, however, consist of imprecise, incomplete, and uncertain data [7]. Because, fuzzy sets concept is often used to handle such data, several fuzzy web mining techniques have been proposed to reveal fuzzy and linguistic knowledge.…”

Section: Introductionmentioning

confidence: 99%

High Fuzzy Utility Strategy Based Webpage Sets Mining from Weblog Database

Kumar¹,

Thakur²

2018

IJIES

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Fuzzy utility mining has been an emerging research issue because of its comprehensibility and fitting to real data sets. Various researches such as association rules mining (ARM), frequent pattern, fuzzy based frequent pattern and utility based frequent pattern mining have been carried out. However, there has been a little research focusing on fuzzy utility based webpage sets mining from weblog databases. Hence, this work high fuzzy utility based webpage sets mining (HFUBWSM) contributes in developing an efficient fuzzy utility strategy to discover frequent webpage sets from web log database. Here, downward closure property in fuzzy sets is applied for pruning the large space by minimum fuzzy utility threshold value (MFUTV) and user defined percentile (UDP). The Experimental evaluation shows that HFUBWSM outperforms the existing algorithms-IHUP, UP-Growth, FHM and HUI-Miner in terms of running time and memory consumption.

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“…Data mining is widely used for deriving knowledge due to some specific applications from databases [11,12]. The most common form is association-rule mining.…”

Section: Introductionmentioning

confidence: 99%

Mining high utility itemsets for transaction deletion in a dynamic database

Lin

Lan

Hong

2015

IDA

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Association-rule mining is used to mine the relationships among the occurrences itemsets in a transactional database. An item is treated as a binary variable whose value is one if it appears in a transaction and zero otherwise. In real-world applications, several products may be purchased at the same time, with each product having an associated profit, quantity, and price. Association-rule mining from a binary database is thus not sufficient in some applications. Utility mining was thus proposed as an extension of frequent-itemset mining for considering various factors from the user. Most utility mining approaches can only process static databases and use batch processing. In real-world applications, transactions are dynamically inserted into or deleted from databases. The Fast UPdated (FUP) algorithm and the FUP2 algorithm were respectively proposed to handle transaction insertion and deletion in dynamic databases. In this paper, a fast-updated high-utility itemsets for transaction deletion (FUP-HUI-DEL) algorithm is proposed to handle transaction deletion for efficiently updating discovered high utility itemsets in decremental mining. The two-phase approach in high utility mining is applied to the proposed FUP-HUI-DEL algorithm for preserving the downward closure property to reduce the number of candidates. The FUP2 algorithm for handling transaction deletion in association-rule mining is adopted in the proposed FUP-HUI-DEL algorithm to reduce the number of scans of the original database in high utility mining. Experiments show that the proposed FUP-HUI-DEL algorithm outperforms the batch two-phase approach.

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A survey of fuzzy web mining

Cited by 37 publications

References 67 publications

An Extensible Web Mining Framework for Real Knowledge

An Extensible Web Mining Framework for Real Knowledge

High Fuzzy Utility Strategy Based Webpage Sets Mining from Weblog Database

Mining high utility itemsets for transaction deletion in a dynamic database

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