scholarly journals TACTICAL ANALYSIS MODELING THROUGH DATA MINING - Pattern Discovery in Racket Sports

2010 ◽  
Keyword(s):  
Data Mining ◽  
Pattern Discovery ◽  
Racket Sports ◽  
Mining Pattern

scholarly journals A Framework for Data Mining Pattern Management

2004 ◽  
pp. 87-98 ◽  
Author(s):  
Barbara Catania ◽  
Anna Maddalena ◽  
Maurizio Mazza ◽  
Elisa Bertino ◽  
Stefano Rizzi
Keyword(s):  
Data Mining ◽  
Mining Pattern

Web Usage Mining and the Challenge of Big Data

Big Data ◽  
2016 ◽  
pp. 899-928
Author(s):  
Abubakr Gafar Abdalla ◽  
Tarig Mohamed Ahmed ◽  
Mohamed Elhassan Seliaman
Keyword(s):  
Data Mining ◽  
Pattern Discovery ◽  
Web Usage Mining ◽  
Data Mining Techniques ◽  
Web Log ◽  
Web Usage ◽  
Web Logs ◽  
Usage Patterns ◽  
Rich Data ◽  
The Web

The web is a rich data mining source which is dynamic and fast growing, providing great opportunities which are often not exploited. Web data represent a real challenge to traditional data mining techniques due to its huge amount and the unstructured nature. Web logs contain information about the interactions between visitors and the website. Analyzing these logs provides insights into visitors' behavior, usage patterns, and trends. Web usage mining, also known as web log mining, is the process of applying data mining techniques to discover useful information hidden in web server's logs. Web logs are primarily used by Web administrators to know how much traffic they get and to detect broken links and other types of errors. Web usage mining extracts useful information that can be beneficial to a number of application areas such as: web personalization, website restructuring, system performance improvement, and business intelligence. The Web usage mining process involves three main phases: pre-processing, pattern discovery, and pattern analysis. Various preprocessing techniques have been proposed to extract information from log files and group primitive data items into meaningful, lighter level abstractions that are suitable for mining, usually in forms of visitors' sessions. Major data mining techniques in web usage mining pattern discovery are: clustering, association analysis, classification, and sequential patterns discovery. This chapter discusses the process of web usage mining, its procedure, methods, and patterns discovery techniques. The chapter also presents a practical example using real web log data.

Applications of Pattern Discovery Using Sequential Data Mining

2012 ◽  
pp. 1-23 ◽  
Author(s):  
Manish Gupta ◽  
Jiawei Han
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Intrusion Detection ◽  
Pattern Mining ◽  
Pattern Discovery ◽  
Sequential Pattern Mining ◽  
Web Usage Mining ◽  
Sequential Pattern ◽  
Sequential Data ◽  
Mining Methods

Sequential pattern mining methods have been found to be applicable in a large number of domains. Sequential data is omnipresent. Sequential pattern mining methods have been used to analyze this data and identify patterns. Such patterns have been used to implement efficient systems that can recommend based on previously observed patterns, help in making predictions, improve usability of systems, detect events, and in general help in making strategic product decisions. In this chapter, we discuss the applications of sequential data mining in a variety of domains like healthcare, education, Web usage mining, text mining, bioinformatics, telecommunications, intrusion detection, et cetera. We conclude with a summary of the work.

Expression and Processing of Inductive Queries

2009 ◽  
pp. 518-526
Author(s):  
Edgard Benítez-Guerrero ◽  
Omar Nieva-García
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Pattern Discovery ◽  
Data Preprocessing ◽  
Knowledge Discovery In Databases ◽  
Digital Information ◽  
Interactive Process ◽  
Useful Knowledge ◽  
Knowledge Based ◽  
Computer Based

The vast amounts of digital information stored in databases and other repositories represent a challenge for finding useful knowledge. Traditionalmethods for turning data into knowledge based on manual analysis reach their limits in this context, and for this reason, computer-based methods are needed. Knowledge Discovery in Databases (KDD) is the semi-automatic, nontrivial process of identifying valid, novel, potentially useful, and understandable knowledge (in the form of patterns) in data (Fayyad, Piatetsky-Shapiro, Smyth & Uthurusamy, 1996). KDD is an iterative and interactive process with several steps: understanding the problem domain, data preprocessing, pattern discovery, and pattern evaluation and usage. For discovering patterns, Data Mining (DM) techniques are applied.

Data Mining Techniques Based on Effective Pattern Discovery

2016 ◽  
Vol 9 (7) ◽  
pp. 197-202
Author(s):  
S. Suresh Babu ◽  
Vahiduddin Shariff ◽  
CH. M. H. Saibaba
Keyword(s):  
Data Mining ◽  
Pattern Discovery ◽  
Data Mining Techniques

scholarly journals Web Usage Mining, Pattern Discovery dan Log File

2014 ◽  
Vol 1 (2) ◽  
Author(s):  
Tri Suratno ◽  
Toni Prahasto ◽  
Adian Fatchur Rochim
Keyword(s):  
Pattern Discovery ◽  
Web Usage Mining ◽  
Web Usage ◽  
Log File ◽  
Mining Pattern

Mining 3D Shape Data for Morphometric Pattern Discovery

2011 ◽  
pp. 1236-1242
Author(s):  
Li Shen ◽  
Fillia Makedon
Keyword(s):  
Data Mining ◽  
Computer Vision ◽  
Pattern Discovery ◽  
3D Models ◽  
Multimedia Databases ◽  
3D Objects ◽  
Digital World ◽  
3D Data ◽  
Shape Indexing ◽  
Technological Advances

Recent technological advances in 3D digitizing, noninvasive scanning, and interactive authoring have resulted in an explosive growth of 3D models in the digital world. There is a critical need to develop new 3D data mining techniques for facilitating the indexing, retrieval, clustering, comparison, and analysis of large collections of 3D models. These approaches will have important impacts in numerous applications including multimedia databases and mining, industrial design, biomedical imaging, bioinformatics, computer vision, and graphics. For example, in similarity search, new shape indexing schemes (e.g. (Funkhouser et al., 2003)) are studied for retrieving similar objects from databases of 3D models. These shape indices are designed to be quick to compute, concise to store, and easy to index, and so they are often relatively compact. In computer vision and medical imaging, more powerful shape descriptors are developed for morphometric pattern discovery (e.g., (Bookstein, 1997; Cootes, Taylor, Cooper, & Graham, 1995; Gerig, Styner, Jones, Weinberger, & Lieberman, 2001; Styner, Gerig, Lieberman, Jones, & Weinberger, 2003)) that aims to detect or localize shape changes between groups of 3D objects. This chapter describes a general shape-based 3D data mining framework for morphometric pattern discovery.

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