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 different data mining techniques can be used in the detection of different forms of fraud. The analysis gave the advantages and disadvantages of using data mining in different operations. The major advantage was that data mining enables the 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 the estimation of a particular target attribute. Descriptive modeling was another technique that entailed dividing data into groups. The other techniques described include pattern mining, used in the identification of rules relating to different data patterns, and anomaly detection, which entails determining the unusual instances that may arise when using the different data-mining models. ) 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 that can be used in the analysis of large volumes of data sets. According to the article, data mining was invented about one and a half decades ago due to advances in artificial intelligence. The Discovery of expert systems, genetic algorithms, neural networks, and machine learning led to developing ways to adapt these schemes and use them for data mining purposes.

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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 security agents in the detection of crimes and terrorist activities (Clifton Web). Regarding the history of data mining, the article stated that data mining was first implemented in credit card fraud detection.

The analysis used both the decision tree algorithm and the clustering algorithm. By using the decision tree algorithm, the information regarding data mining techniques was grouped by making use of predefined knowledge. The analysis entails a description of different crime detection techniques. Moreover, the most appropriate technique for the detection of different types of crimes was suggested based on the profitability of using any single technique. Using the clustering technique, the data was divided into different groups to obtain certain patterns. Such patterns included the classification of data mining techniques based on their uses. This was used to develop ways in which the different techniques can be applied in business (Clifton Web).

The analysis identified the various data mining techniques, their applications, strengths, and weaknesses. The analysis was important to the business world. For example, the analysis of the use of data mining in the detection of credit card fraud identified the challenges involved in the process. This was crucial since it gave insights into how different techniques can be developed to make data mining more effective in credit card fraud detection.

Another reason why the analysis was important to the business world was that it analyzed the different data mining approaches, such as predictive modeling, descriptive modeling, pattern mining, and anomaly detection. The analysis explained how the different techniques work. Moreover, the analysis was crucial since it provided insights into how different techniques can be used in the detection of fraud crime in different types of business transactions. Moreover, it highlighted the shortcoming of the different techniques. This is crucial since it provides intuitions on areas that can be improved to make the techniques more effective (Clifton Web).

An additional reason why the analysis was important is that it pinpointed the issues that arise when using data mining techniques in fraud detection. One such issue is a privacy concern. This was crucial since it gave insights into how the business world can continue using data mining techniques to combat crime without risking the loss of reputation. Moreover, the companies can use data mining for fraud detection crimes while making fewer errors, such as those of biases (Clifton Web). Conclusion Data mining has undergone modification with technological advancement. Data mining plays a great role in enabling the detection of problems such as fraud.

This is because it enables the analysis of large and complex quantities of data. In the article about data mining, Clifton used both decision tree and cluster analysis to assess the different types of data mining. By using a decision tree, the author group data mining based on the techniques used. By using clustering, the data were grouped to obtain certain patterns. The analysis was important to the business world since it provided insights into how the different data mining techniques work.

References

1. Clifton, Christopher. "Data mining."
2. Encyclopedia Britannica (n. d. ): 1-3. Web.

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