Web Usage Mining for UUM Learning Care Using Association Rules

The enormous of information on the World Wide Web makes it obvious candidate for data mining research. Application of data mining techniques to the World Wide Web referred as Web mining where this term has been used in three distint ways; Web Content Mining, Web Structure Mining and Web Usage Mining. E-Learning is one of the Web based application where it will facing with large amount of data. In order to produce the university E-Learning (UUM Educare) portal usage patterns and user behaviors, this paper implements the high level process of Web usage mining using basic Association Rules algorithm - Apriori Algorithm. Web usage mining consists of three main phases, namely Data Preprocessing. Pattern Discovering and Patern Analysis. Main resources, server log files become a set of raw data where it's must go through with all the Web usage mining technique, Web usage mining approach has been combined with the basic Association Rules, Apriori Algorithm to optimize the content of the university E-Learning portal. Finally this paper will present an overview of results with the analysis and Web administrator can use the findings for the suitable valuable actions

Web usage mining for UUM learning care using association rules

The enormous content of information on the World Wide Web makes it obvious candidate for data mining research. Application of data milling techniques to the World Wide Web referred as Web mining where this term has been used in three distinct ways; Web Content Mining, Web Structure Mining and Web Usage Mining. E-Leaming is one of the Web based application where it will facing with large amount of data. In order to produce the university E-Learning (UUM Educare) portal usage patterns and user behaviors, this paper implements the high level process of Web usage mining using basic Association Rules algorithm - Apriori Algorithm. Web usage mining consists of three main phases, namely Data Preprocessing, Pattern Discovering and Pattern Analysis. Main resources, server log files become a set of raw data where it's must go through with all the Web usage mining phases to produce the final results — set of rides. With the powerful of data mining technique, Web usage mining approach has been combined with the basic Association Rules, Apriori Algorithm to optimize the content of the university E�Learning portal. Finally, this paper will present an overview of results with the analysis and Web administrator can use the findings for the suitable valuable actions

Analisis dan Implementasi Web Usage Mining Menggunakan Algoritma Graph Partitioning (Studi Kasus : Tuneeca Online Store)

ABSTRAKSI: Peningkatan aktivitas kunjungan terhadap website menghasilkan data yang cukup banyak mengenai user dan interaksinya dengan website yang disimpan dalam web server log. Informasi yang bisa diperoleh salah satunya adalah pola navigasi user. Pola navigasi user menggambarkan aktivitas apa saja yang dilakukan user selama mengakses suatu website. Memahami pola navigasi user dalam mengakses suatu website dapat berguna untuk memahami tingkah laku user dalam mengakses websitetersebut. Sehingga dapat digunakan sebagai acuan dalam perbaikan kualitas website dan menjamin kepuasan user ketika menggunakan website tersebut.Pada ranah e-commerce, pola navigasi user dapat digunakan sebagai acuan untuk menentukan strategi bisnis berdasarkan tingkah laku user yang diperoleh. Dalam tugas akhir ini, web server logdari tuneeca online storeakan diproses dengan mengimplementasikan salah satu metode dalamweb usage mining yaitu clustering.Web usage mining merupakan salah satu pengaplikasian teknik data mining yang dapat digunakan untuk menemukan pola navigasi user. Data log tersebut akan melalui tahap preprocessing, kemudian dilakukan clustering terhadap page dengan menggunakan algoritma graph partitioning. Hasil penelitian menunjukkan bahwa penentuan parameter nilai minimum bobot mempengaruhi jumlah klaster yang dihasilkan serta nilai visit coherence yang diperoleh. Performansi dari algoritma graph partitioning cukup baik dalam membentuk klaster pola navigasi berdasarkan tingginya nilai modularization qualityyang diperoleh. Pola navigasi user yang dihasilkan dapat digunakan sebagai acuan untuk rekomendasi pengembangan web dari tuneeca online store.KATA KUNCI: web usage mining, pola navigasi user,web server log, graph partitioning, visit coherence, modularization qualityABSTRACT: Increased activity of a visit to the website generates huge enough data about users and their interaction with a website that is stored in the web server logs. One of the information that can be obtained is user navigation patterns. User navigation patternsgenerated, could give an overview about what users actually do and need when access the website. Understanding the user navigation patternscan be useful for understanding user behavior in accessing the website. So it can be used as a reference in improving the quality of the website and ensure user satisfaction when using the website. In the domain of e-commerce, user navigation patterns can be used as a reference for determining a business strategy based on user behavior is obtained. In this final project, the web server logs of tuneeca online storewill be processed by implementing clustering, one of web usage mining methods.Web usage mining is one of the application of data mining techniques that can be used to discover the user navigation patterns. The log data will be going through the preprocessing stage, then performed clustering to the pages by using graph partitioning algorithm. The result shows that determining the minimum weight value affects the number of clusters produced and the visit coherence value obtained. Performance of graph partitioning algorithm is quite good in forming clusters of navigation patterns based on high value of modularization quality obtained. User navigation patterns generated can be used as a reference for the recommendation of web development Tuneeca online store.KEYWORD: web usage mining, user navigation patterns, web server log, graph partitioning, visit coherence, modularization qualit

Binary Particle Swarm Optimization based Biclustering of Web usage Data

Web mining is the nontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques or methods. It may give information that is useful for improving the services offered by web portals and information access and retrieval tools. With the rapid development of biclustering, more researchers have applied the biclustering technique to different fields in recent years. When biclustering approach is applied to the web usage data it automatically captures the hidden browsing patterns from it in the form of biclusters. In this work, swarm intelligent technique is combined with biclustering approach to propose an algorithm called Binary Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The main objective of this algorithm is to retrieve the global optimal bicluster from the web usage data. These biclusters contain relationships between web users and web pages which are useful for the E-Commerce applications like web advertising and marketing. Experiments are conducted on real dataset to prove the efficiency of the proposed algorithms

WEB MINING IN E-COMMERCE

Recently, the web is becoming an important part of people’s life. The web is a very good place to run successful businesses. Selling products or services online plays an important role in the success of businesses that have a physical presence, like a reE-Commerce, Data mining, Web mining

Datamining for Web-Enabled Electronic Business Applications

Web-Enabled Electronic Business is generating massive amount of data on customer purchases, browsing patterns, usage times and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for web-enabled electronic-business

Web Usage Mining with Evolutionary Extraction of Temporal Fuzzy Association Rules

In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the intersection of fuzzy sets' boundaries where there is less support (lower membership), so the rules are lost. A genetic algorithm (GA)-based solution is described that uses the flexible nature of the 2-tuple linguistic representation to discover rules that occur at the intersection of fuzzy set boundaries. The GA-based approach is enhanced from previous work by including a graph representation and an improved fitness function. A comparison of the GA-based approach with a traditional approach on real-world Web log data discovered rules that were lost with the traditional approach. The GA-based approach is recommended as complementary to existing algorithms, because it discovers extra rules. (C) 2013 Elsevier B.V. All rights reserved