Analysis of Data Mining

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Analysis of Data Mining

The article Data Mining by Christopher Clifton analyzed how different types of data mining techniques have been applied in crime detection and different outcomes. Moreover, the analysis proposed how the different data mining techniques can be used in detection of different form of frauds. The analysis gave the advantages and disadvantages of using data mining in different operation. The major advantage was that data mining enables analysis of large quantities of data. This is important since such data cannot be analyzed manually since the data is often complex for humans to understand. However, data mining techniques have been used for deceitful purposes such as inappropriate disclosure of private information. The article analyzed different data mining techniques. Predictive modeling is one such technique used in estimation of particular target attribute. Descriptive modeling was another technique, which entails dividing data into groups. The other techniques described include pattern mining used in identification of rules relating to different data pattern and anomaly detection, which entails determining the unusual instances that, may arise when using the different data-mining model. 1) What is the title and what was the objective of the study/analysis) The title of the article was data mining. The article focused on skills in knowledge discovery can be used in analysis of large volumes of data sets. According to the article, data mining was invented about one and a half decades ago due to the advances in artificial intelligence. Discovery of expert system, genetic algorithms, neural networks, and machine leaning led to develop ways to adapt these schemes and use them for data mining purposes. The objective of the article was to give a history of data mining, the different types of data mining and the application of data mining in different fields such as business, scientific research, as well as by...
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