Data mining: an exploratory overview.
- Authors: Ferreira, Rian Johan
- Date: 2008-04-22T06:17:29Z
- Subjects: business intelligence , data mining
- Type: Thesis
- Identifier: uj:8535 , http://hdl.handle.net/10210/268
- Description: Managers the world over complain that they are overwhelmed by the amount of data available to them, but that they are unable to make any sense of this data. The changing business environment and the fact that customers are becoming more and more demanding highlight the need for organisations to be able to adapt faster and more effectively to those changes. Data mining developed as a direct result of the natural evolution of information technology. The increased organisational use of computer based systems has resulted in the accumulation of vast amounts of data, and the need for decision makers to have efficient access to knowledge, and not only data, has resulted in more and more organisations adopting the use of data mining. The promise of data mining is to return the focus of large, impersonal organisations to serving their customers better and to providing more efficient business processes. Indeed, for some organisations data mining offers the potential for gaining a competitive advantage, but for others it has become a matter of survival. The literature is filled with examples of the successful application of data mining, not only to specific business functions, but also in specific industries. Undoubtedly, certain industries, such as those dealing with huge amounts of data, and those exposed to many diverse customers, stand to benefit more from data mining than others. iii The benefits, associated with data mining, for organisations, individuals and society as a whole, far exceed its drawbacks, but the biggest issue facing organisations that want to employ data mining, is its cost. The other drawbacks of data mining relate to the threat that it poses to privacy, and any data mining effort must not only be done within the framework of the relevant laws, but must also be done in an ethical manner. Although data mining is probably beyond the financial ability of most organisations, its main principle, the fact that there might be value in organisational data, should not be forgotten. Organisations must endeavour to treat their data with the same respect it has for all its other corporate assets. , Mr. C. Scheepers
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A multi-agent collaborative personalized web mining system model.
- Authors: Oosthuizen, Ockmer Louren
- Date: 2008-06-02T13:08:00Z
- Subjects: data mining , web search engines , web usage mining , World Wide Web , intelligent agents (computer software) , knowledge , information retrieval
- Type: Thesis
- Identifier: uj:8737 , http://hdl.handle.net/10210/508
- Description: The Internet and world wide web (WWW) have in recent years, grown exponentially in size and in terms of the volume of information that is available on it. In order to effectively deal with the huge amount of information on the web, so called web search engines have been developed for the task of retrieving useful and relevant information for its users. Unfortunately, these web search engines have not kept pace with the boom growth and commercialization of the web. The main goal of this dissertation is the development of a model for a collaborative personalized meta-search agent (COPEMSA) system for the WWW. This model will enable the personalization of web search for users. Furthermore, the model aims to leverage on current search engines on the web as well as enable collaboration between users of the search system for the purposes of sharing useful resources between them. The model also employs the use of multiple intelligent agents and web content mining techniques. This enables the model to autonomously retrieve useful information for it’s user(s) and present this information in an effective manner. In order to achieve the above stated, the COPEMSA model employs the use of multiple intelligent agents. COPEMSA consists of five core components: a user agent, a query agent, a community agent, a content mining agent and a directed web spider. The user agent learns about the user in order to introduce personal preference into user queries. The query agent is a scaled down meta-search engine with the task of submitting the personalized queries it receives from the user agent to multiple search services on theWWW. The community agent enables the search system to communicate and leverage on the search experiences of a community of searchers. The content mining agent is responsible for analysis of the retrieved results from theWWWand the presentation of these results to the system user. Finally, a directed web spider is used by the content mining agent to retrieve the actual web pages it analyzes from the WWW. In this dissertation an additional model is also presented to deal with a specific problem all web spidering software must deal with namely content and link encapsulation. , Prof. E.M. Ehlers
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