By Kweku-Muata Osei-Bryson, Corlane Barclay
Delivering insights into the scope of knowledge mining projects, this article examines their socio-economic and felony implications to stakeholders, agencies and society.
summary: delivering insights into the scope of knowledge mining projects, this article examines their socio-economic and felony implications to stakeholders, businesses and society
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Extra resources for Knowledge Discovery Process and Methods to Enhance Organizational Performance
Sample text
Deploy models. Assess results. Begin again. The Five-Step Process Model Step 1—Determination of business objectives: This step involves clearly defining the business problems or challenges. The minimum requirements are a perceived business problem or opportunity and some level of executive sponsorship. Expectations are usually established during this step in the process. Step 2—Data preparation: Cabena et al. (1998) note that data preparation is the most resource-consuming step in the process, typically requiring up to 60% of the effort of the entire project.
Phase 5—Evaluation: This phase of the project consists of thoroughly evaluating the model and review the steps executed to construct the model to be certain that it properly achieves the business objectives. A key objective is to determine if there is some important business issue that has not been sufficiently considered. At the end of this phase, a decision on the use of the DM results should be reached. Phase 6—Deployment: Creation of the model is generally not the end of the project. Even if the purpose of the model is to increase knowledge of the data, the knowledge gained will need to be organized and presented in a way that the customer can use it.
2. Understanding the Problem Domain In this step, one works closely with domain experts to define the problem and determine the project goals, identify key people, and learn about current solutions to the problem. It involves learning domain-specific terminology. A description of the problem including its restrictions is done. The project goals then need to be translated into the KDDM goals, and may include the initial selection of the potential DM tools. Understanding the Data This step includes collection of sample data and deciding the appropriate data, including its format and size.
