scholarly journals Data mining software using fuzzy inference systems at the World Wide Web

Data Mining X ◽  
2009 ◽  
Author(s):  
R. C. F. Vidal ◽  
N. F. F. Ebecken ◽  
A. G. Evsukoff
Keyword(s):  
Data Mining ◽  
World Wide Web ◽  
World Wide ◽  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
The World ◽  
Inference Systems

Analytical Competition for Managing Customer Relations

2011 ◽  
pp. 25-30 ◽  
Author(s):  
Dan Zhu
Keyword(s):  
Data Mining ◽  
World Wide Web ◽  
Text Mining ◽  
World Wide ◽  
Customer Relationship ◽  
Service Access ◽  
Web Content ◽  
The World ◽  
Content Mining ◽  
Automated Tools

With the advent of technology, information is available in abundance on the World Wide Web. In order to have appropriate and useful information users must increasingly use techniques and automated tools to search, extract, filter, analyze and evaluate desired information and resources. Data mining can be defined as the extraction of implicit, previously unknown, and potentially useful information from large databases. On the other hand, text mining is the process of extracting the information from an unstructured text. A standard text mining approach will involve categorization of text, text clustering, and extraction of concepts, granular taxonomies production, sentiment analysis, document summarization, and modeling (Fan et al, 2006). Furthermore, Web mining is the discovery and analysis of useful information using the World Wide Web (Berry, 2002; Mobasher, 2007). This broad definition encompasses “web content mining,” the automated search for resources and retrieval of information from millions of websites and online databases, as well as “web usage mining,” the discovery and analysis of users’ website navigation and online service access patterns. Companies are investing significant amounts of time and money on creating, developing, and enhancing individualized customer relationship, a process called customer relationship management or CRM. Based on a report by the Aberdeen Group, worldwide CRM spending reached close to $20 billion by 2006. Today, to improve the customer relationship, most companies collect and refine massive amounts of data available through the customers. To increase the value of current information resources, data mining techniques can be rapidly implemented on existing software and hardware platforms, and integrated with new products and systems (Wang et al., 2008). If implemented on high-performance client/server or parallel processing computers, data mining tools can analyze enormous databases to answer customer-centric questions such as, “Which clients have the highest likelihood of responding to my next promotional mailing, and why.” This paper provides a basic introduction to data mining and other related technologies and their applications in CRM.

scholarly journals Building Java Intelligent Applications Data Mining for Java Type-2 Fuzzy Inference Systems

2015 ◽  
Vol 51 ◽  
pp. 2719-2728 ◽  
Author(s):  
Manuel Castañón-Puga ◽  
Josué Miguel Flores-Parra ◽  
Juan Ramón Castro ◽  
Carelia Gaxiola-Pacheco ◽  
Luis Enrique Palafox-Maestre
Keyword(s):  
Data Mining ◽  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Inference Systems

Reinforcing CRM with Data Mining

2011 ◽  
pp. 950-954
Author(s):  
Dan Zhu
Keyword(s):  
Data Mining ◽  
World Wide Web ◽  
Customer Relationship Management ◽  
World Wide ◽  
Relationship Management ◽  
Customer Relationships ◽  
Customer Relationship ◽  
The World ◽  
Automated Tools

With the explosive growth of information available on the World Wide Web, users must increasingly use automated tools to find, extract, filter, and evaluate desired information and resources. Companies are investing significant amounts of time and money on creating, developing, and enhancing individualized customer relationships, a process called customer relationship management, or CRM (Berry & Linoff, 1999; Buttle, 2003; Rud, 2000). Based on a report by the Aberdeen Group, worldwide CRM spending reached $13.7 billion in 2002 and should be close to $20 billion by 2006.

Metadata Management

Web Mining ◽  
2011 ◽  
pp. 1-26 ◽  
Author(s):  
Gilbert W. Laware
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
World Wide Web ◽  
World Wide ◽  
Metadata Management ◽  
Web Content ◽  
Current Information ◽  
The World ◽  
Increasing Demand

This chapter introduces the need for the World Wide Web to provide a standard mechanism so individuals can readily obtain data, reports, research and knowledge about any topic posted to it. Individuals have been frustrated by this process since they are not able to access relevant data and current information. Much of the reason for this lies with metadata, the data about the data that are used in support of Web content. These metadata are non-existent, ill-defined, erroneously labeled, or, if well-defined, continue to be marked by other disparate metadata. With the ever-increasing demand for Web-enabled data mining, warehousing and management of knowledge, an organization has to address the multiple facets of process, standards, technology, data mining, and warehousing management. This requires approaches to provide an integrated interchange of quality metadata that enables individuals to access Web content with the most relevant, contemporary data, information, and knowledge that are both content-rich and practical for decision-making situations.

Metadata Management

2011 ◽  
pp. 1305-1330
Author(s):  
Gilbert W. Laware
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
World Wide Web ◽  
World Wide ◽  
Metadata Management ◽  
Web Content ◽  
Current Information ◽  
The World ◽  
Increasing Demand

This chapter introduces the need for the World Wide Web to provide a standard mechanism so individuals can readily obtain data, reports, research and knowledge about any topic posted to it. Individuals have been frustrated by this process since they are not able to access relevant data and current information. Much of the reason for this lies with metadata, the data about the data that are used in support of Web content. These metadata are non-existent, ill-defined, erroneously labeled, or, if well-defined, continue to be marked by other disparate metadata. With the ever-increasing demand for Web-enabled data mining, warehousing and management of knowledge, an organization has to address the multiple facets of process, standards, technology, data mining, and warehousing management. This requires approaches to provide an integrated interchange of quality metadata that enables individuals to access Web content with the most relevant, contemporary data, information, and knowledge that are both content-rich and practical for decision-making situations.

LARGE-SCALE MINING, DISCOVERY AND VISUALIZATION OF WWW USER CLICKPATHS

2002 ◽  
Vol 02 (01) ◽  
pp. 21-48 ◽  
Author(s):  
BRENDAN KITTS ◽  
KEVIN HETHERINGTON-YOUNG ◽  
MARTIN VRIEZE
Keyword(s):  
Data Mining ◽  
World Wide Web ◽  
Computer Graphics ◽  
Association Rules ◽  
Large Scale ◽  
World Wide ◽  
Visual Representation ◽  
Graph Layout ◽  
The World ◽  
Commercial Sources

Analysis of user clickstreams on the World Wide Web is made challenging by the volume of data and the difficulty of visualizing millions of different navigation paths. We present a method for identifying user clickpaths which scales well on large amounts of data, and provides an intuitive and insightful visual representation of user activity. Our technique borrows from the data mining literature on association rules and the computer graphics literature on graph layout optimization. The method is demonstrated with data from two commercial sources and paints a fascinating picture of web activity.

Mining Association Rules from XML Data

2008 ◽  
pp. 59-71 ◽  
Author(s):  
Qin Ding ◽  
Gnanasekaran Sundarraj
Keyword(s):  
Data Mining ◽  
World Wide Web ◽  
Association Rules ◽  
World Wide ◽  
Structured Data ◽  
Rule Mining ◽  
Xml Data ◽  
Xml Documents ◽  
The World ◽  
Tools And Techniques

With the growing usage of XML in the World Wide Web and elsewhere as a standard for the exchange of data and to represent semi-structured data, there is an imminent need for tools and techniques to perform data mining on XML documents and XML repositories. In this chapter, we propose a framework for association rule mining on XML data. We present a Java-based implementation of the Apriori and the FP-Growth algorithms for this task and compare their performances. We also compare the performance of our implementation with an XQuery-based implementation.

Metadata Management

2008 ◽  
pp. 3416-3439
Author(s):  
Gilbert W. Laware
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
World Wide Web ◽  
World Wide ◽  
Metadata Management ◽  
Web Content ◽  
Current Information ◽  
The World ◽  
Increasing Demand

This chapter introduces the need for the World Wide Web to provide a standard mechanism so individuals can readily obtain data, reports, research and knowledge about any topic posted to it. Individuals have been frustrated by this process since they are not able to access relevant data and current information. Much of the reason for this lies with metadata, the data about the data that are used in support of Web content. These metadata are non-existent, ill-defined, erroneously labeled, or, if well-defined, continue to be marked by other disparate metadata. With the ever-increasing demand for Web-enabled data mining, warehousing and management of knowledge, an organization has to address the multiple facets of process, standards, technology, data mining, and warehousing management. This requires approaches to provide an integrated interchange of quality metadata that enables individuals to access Web content with the most relevant, contemporary data, information, and knowledge that are both content-rich and practical for decision-making situations.

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