Journal ArticleDOI
Web usage mining for Web site evaluation
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TLDR
Making a site better fit its users by knowing about the needs of potential customers and the ability to establish personalized services that satisfy these needs is key to winning this competitive race.Abstract:
T he Web has become a borderless marketplace for purchasing and exchanging goods and services. While Web users search for, inspect and occasionally purchase products and services on the Web, companies compete bitterly for each potential customer. The key to winning this competitive race is knowledge about the needs of potential customers and the ability to establish personalized services that satisfy these needs. Making a site better fit its users.read more
Citations
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Journal ArticleDOI
A Comprehensive Survey of Pattern Mining: Challenges and Opportunities
TL;DR: The main goal of this paper is to present both an introduction to all pattern mining and a survey of various algorithms, challenges and research opportunities.
Book ChapterDOI
Web Usage Mining: Algorithms and Results
TL;DR: This chapter focuses on the mining of Web access logs, commonly known as Web usage mining, and analyzes algorithms for preprocessing and extracting knowledge from such logs and proposes its own techniques to mine the logs in a more holistic manner.
Proceedings ArticleDOI
Mining association rules uses fuzzy weighted FP-growth
TL;DR: This paper attempts to use fuzzy partition method and decide membership function of quantitative values of each transaction item and is expected to improve Apriori algorithm for its better efficiency of the whole association rules.
Proceedings Article
Developing a Framework for Web Analytics
TL;DR: This paper proposes a framework for establishing sustainable WA programmes, which widens the focus of the WA process to fully account for business requirements and to focus attention on achieving actionable outcomes.
A Survey on Preprocessing Techniques in Web Usage Mining
TL;DR: An overview of data preprocessing techniques aiming at identifying unique users, user sessions and transactions is presented in this survey, which consists of three phases, namely preprocessing, pattern discovery, and pattern analysis.
References
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Proceedings ArticleDOI
Mining sequential patterns
TL;DR: Three algorithms are presented to solve the problem of mining sequential patterns over databases of customer transactions, and empirically evaluating their performance using synthetic data shows that two of them have comparable performance.
Book
Data Mining Techniques: For Marketing, Sales, and Customer Support
TL;DR: One of the first practical guides to mining business data, Data Mining Techniques describes techniques for detecting customer behavior patterns useful in formulating marketing, sales, and customer support strategies.
Journal ArticleDOI
Data Preparation for Mining World Wide Web Browsing Patterns
TL;DR: This paper presents several data preparation techniques in order to identify unique users and user sessions and Transactions identified by the proposed methods are used to discover association rules from real world data using the WEBMINER system.
Proceedings Article
Discovering generalized episodes using minimal occurrences
Heikki Mannila,Hannu Toivonen +1 more
TL;DR: A general and flexible framework of specifying classes of generalized episodes that are recurrent combinations of events satisfying certain conditions is presented, which can be instantiated to a wide variety of applications by selecting suitable primitive conditions.