Integrating Knowledge Engineering and Data Mining in e-commerce Fraud Prediction

2013 ◽  
pp. 460-466
Author(s):  
Timo Polman ◽  
Marco Spruit
Keyword(s):  
Data Mining ◽  
Knowledge Engineering ◽  
Fraud Prediction

Data Mining with Incomplete Data

2011 ◽  
pp. 293-296 ◽  
Author(s):  
Hai Wang ◽  
Shouhong Wang
Keyword(s):  
Data Mining ◽  
Missing Data ◽  
Survey Data ◽  
Incomplete Data ◽  
Knowledge Engineering ◽  
Data Set ◽  
Survey Question ◽  
Data Source ◽  
The Common ◽  
Extent Of Damage

Survey is one of the common data acquisition methods for data mining (Brin, Rastogi & Shim, 2003). In data mining one can rarely find a survey data set that contains complete entries of each observation for all of the variables. Commonly, surveys and questionnaires are often only partially completed by respondents. The possible reasons for incomplete data could be numerous, including negligence, deliberate avoidance for privacy, ambiguity of the survey question, and aversion. The extent of damage of missing data is unknown when it is virtually impossible to return the survey or questionnaires to the data source for completion, but is one of the most important parts of knowledge for data mining to discover. In fact, missing data is an important debatable issue in the knowledge engineering field (Tseng, Wang, & Lee, 2003).

Arithmetic Research on Data Mining Technology and Associative Rules Mining

2014 ◽  
Vol 556-562 ◽  
pp. 3949-3951
Author(s):  
Jian Xin Zhu
Keyword(s):  
Data Mining ◽  
High Performance ◽  
Knowledge Engineering ◽  
Business Decisions ◽  
Depth Data ◽  
Source Data ◽  
Wide Availability ◽  
High Level ◽  
Performance Computing ◽  
Level Analysis

Data mining is a technique that aims to analyze and understand large source data reveal knowledge hidden in the data. It has been viewed as an important evolution in information processing. Why there have been more attentions to it from researchers or businessmen is due to the wide availability of huge amounts of data and imminent needs for turning such data into valuable information. During the past decade or over, the concepts and techniques on data mining have been presented, and some of them have been discussed in higher levels for the last few years. Data mining involves an integration of techniques from database, artificial intelligence, machine learning, statistics, knowledge engineering, object-oriented method, information retrieval, high-performance computing and visualization. Essentially, data mining is high-level analysis technology and it has a strong purpose for business profiting. Unlike OLTP applications, data mining should provide in-depth data analysis and the supports for business decisions.

Formal Knowledge Engineering Methods for Knowledge Discovery

2002 ◽  
Vol 01 (02) ◽  
pp. 141-154
Author(s):  
Satheesh Ramachandran
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Life Cycle ◽  
Decision Support ◽  
Knowledge Discovery ◽  
Knowledge Engineering ◽  
The Other ◽  
Full Potential ◽  
Formal Knowledge

This paper presents a framework for the integrated use of formal knowledge engineering methods and data mining based knowledge discovery methods. Knowledge is a key enterprise asset, and organizations are adopting both knowledge engineering and knowledge discovery paradigms for better knowledge management and enhanced decision support capability. Although there exists a useful interdependence between these endeavors, not much effort has been focused on using the full potential of one for the other. This paper presents a framework for the integrated use of established formal knowledge engineering methods and knowledge discovery processes with the ultimate intent of better managing the enterprise knowledge life cycle. It provides a brief overview of the knowledge discovery processes, and introduces a class of formal knowledge engineering methods and the perceived role of these methods in supporting the integration between the two worlds of knowledge discovery and knowledge engineering.

Data Mining with Incomplete Data

2011 ◽  
pp. 526-530 ◽  
Author(s):  
Hai Wang ◽  
Shouhong Wang
Keyword(s):  
Data Mining ◽  
Missing Data ◽  
Incomplete Data ◽  
Missing Values ◽  
Knowledge Engineering ◽  
Data Set ◽  
Survey Question ◽  
Data Source ◽  
The Common ◽  
Extent Of Damage

Survey is one of the common data acquisition methods for data mining (Brin, Rastogi & Shim, 2003). In data mining one can rarely find a survey data set that contains complete entries of each observation for all of the variables. Commonly, surveys and questionnaires are often only partially completed by respondents. The possible reasons for incomplete data could be numerous, including negligence, deliberate avoidance for privacy, ambiguity of the survey question, and aversion. The extent of damage of missing data is unknown when it is virtually impossible to return the survey or questionnaires to the data source for completion, but is one of the most important parts of knowledge for data mining to discover. In fact, missing data is an important debatable issue in the knowledge engineering field (Tseng, Wang, & Lee, 2003). In mining a survey database with incomplete data, patterns of the missing data as well as the potential impacts of these missing data on the mining results constitute valuable knowledge. For instance, a data miner often wishes to know how reliable a data mining result is, if only the complete data entries are used; when and why certain types of values are often missing; what variables are correlated in terms of having missing values at the same time; what reason for incomplete data is likely, etc. These valuable pieces of knowledge can be discovered only after the missing part of the data set is fully explored.

Data Mining with Incomplete Data

2008 ◽  
pp. 3027-3032
Author(s):  
Hai Wang ◽  
Shouhong Wang
Keyword(s):  
Data Mining ◽  
Missing Data ◽  
Survey Data ◽  
Incomplete Data ◽  
Knowledge Engineering ◽  
Data Set ◽  
Survey Question ◽  
Data Source ◽  
The Common ◽  
Extent Of Damage

Survey is one of the common data acquisition methods for data mining (Brin, Rastogi & Shim, 2003). In data mining one can rarely find a survey data set that contains complete entries of each observation for all of the variables. Commonly, surveys and questionnaires are often only partially completed by respondents. The possible reasons for incomplete data could be numerous, including negligence, deliberate avoidance for privacy, ambiguity of the survey question, and aversion. The extent of damage of missing data is unknown when it is virtually impossible to return the survey or questionnaires to the data source for completion, but is one of the most important parts of knowledge for data mining to discover. In fact, missing data is an important debatable issue in the knowledge engineering field (Tseng, Wang, & Lee, 2003).

Capability Maturity

2011 ◽  
pp. 1117-1124
Author(s):  
Alfs T. Berztiss
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Knowledge Representation ◽  
Organizational Structure ◽  
Knowledge Engineering ◽  
Rapid Change ◽  
Insurance Company ◽  
Capability Maturity ◽  
Recent Origin ◽  
To Come

The dependence of any organization on knowledge management is clearly understood. Actually, we should distinguish between knowledge management (KM) and knowledge engineering (KE): KM is to define and support organizational structure, allocate personnel to tasks, and monitor knowledge engineering activities; KE is concerned with technical matters, such as tools for knowledge acquisition, knowledge representation, and data mining. We shall use the designation KMKE for knowledge management and knowledge engineering collectively. KM is a very young area—the three articles termed “classic works” in Morey, Maybury, and Thuraisingham (2000) date from 1990, 1995, and 1996, respectively. We could regard 1991 as the start of institutionalized KM. This is when the Skandia AFS insurance company appointed a director of intellectual capital. KE has a longer history—expert systems have been in place for many years. Because of its recent origin, KMKE is characterized by rapid change. To deal with the change, we need to come to a good understanding of the nature of KMKE.

Management of Data Streams for Large-Scale Data Mining

2011 ◽  
pp. 136-153
Author(s):  
Jon R. Wright ◽  
Gregg T. Vesonder ◽  
Tamraparni Dasu
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Information Acquisition ◽  
Large Scale ◽  
Knowledge Engineering ◽  
Mining Operation ◽  
Large Scale Data ◽  
Technical Issues ◽  
Automate Information ◽  
Scale Data

In an enterprise setting, a major challenge for any data mining operation is managing data streams or feeds, both data and metadata, to ensure a stable and certifiably accurate flow of data. Data feeds in this environment can be complex, numerous and opaque. The management of frequently changing data and metadata presents a considerable challenge. In this paper, we articulate the technical issues involved in the task of managing enterprise data and propose a multi-disciplinary solution, derived from fields such as knowledge engineering and statistics, to understand, standardize, and automate information acquisition and quality management in preparation for enterprise mining.

scholarly journals Neural networks in data mining

2012 ◽  
Vol 49 (No. 9) ◽  
pp. 427-431 ◽  
Author(s):  
AVeselý
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Knowledge Engineering ◽  
Data Sets ◽  
Data Mining Algorithm ◽  
Self Organizing Map ◽  
New Methods ◽  
Area Of Interest ◽  
Database Theory ◽  
Self Organizing

To posses relevant information is an inevitable condition for successful enterprising in modern business. Information could be parted to data and knowledge. How to gather, store and retrieve data is studied in database theory. In the knowledge engineering, there is in the centre of interest the knowledge and methods of its formalization and gaining are studied. Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining. Classical methods of gaining knowledge from data sets are statistical methods. In data mining, new methods besides statistical are used. These new methods have their origin in artificial intelligence. They look for unknown and unexpected relations, which can be uncovered by exploring of data in database. In the article, a utilization of modern methods of data mining is described and especially the methods based on neural networks theory are pursued. The advantages and drawbacks of applications of multiplayer feed forward neural networks and Kohonen’s self-organizing maps are discussed. Kohonen’s self-organizing map is the most promising neural data-mining algorithm regarding its capability to visualize high-dimensional data.

Sign in / Sign up

Export Citation Format

Share Document