Analysis of Data Mining

ITKM Analysis of info Mining The member selective information Mining by Christopher Clifton analyzed how incompatible types of entropy exploit techniques have been applied in crime signal sensing and diametrical outcomes. Moreover, the abstract proposed how the different info tap techniques hatful be utilize in detection of different form of frauds. The analytic thinking gave the advantages and disadvantages of bristlement information mine in different operation. The major advantage was that selective information exploit enables analysis of large quantities of entropy. This is weighty since such data cannot be analyzed manually since the data is often complex for humans to understand.However, data exploit techniques have been used for deceitful purposes such as hostile disclosure of private information. The article analyzed different data mining techniques. Predictive modeling is genius such technique used in estimation of particular target attribute. Desc riptive modeling was another technique, which entails dividing data into groups. The other techniques advertd include pattern mining used in identification of rules relating to different data pattern and anomaly detection, which entails determining the droll instances that, may arise when use the different data-mining model. ) What is the title and what was the objective of the charter/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. breakthrough of expert system, genetic algorithmic programs, neural networks, and machine leaning led to develop ways to adapt these schemes and use them for data mining purposes.Related article What Business Can Learn From Text MiningThe objective of the article was to unwrap a history of data mining, the di fferent types of data mining and the screening of data mining in different fields such as bank line, scientific research, as well as by security agents in 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 2) What data mining algorithm was used (i. e. cluster analysis, decision channelise, neural network, other) and describe the algorithm?The analysis used both decision tree algorithm and clustering algorithm. By using decision tree algorithm, the information regarding data mining techniques was grouped by making use of predefined knowledge. The analysis entails definition of different crime detection techniques. Moreover, the most appropriate technique for detection of different types of crimes was suggested ground on the profitability of using any one technique. Using clustering technique, the data was divided into different groups to o btain real patterns. Such pattern included classification to 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). 3) What was the outcome of the analysis, and how did it benefit the business, if in that location was a benefit? The analysis place the various data mining techniques, their applications, strengths and weaknesses. The analysis was important to the business world. For example, the analysis on use of data mining in detection of credit card fraud identified the challenges involved on the process. This was crucial since it gave insights on how different techniques can be developed to make data mining more effective in credit card fraud detection.Another reason wherefore 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 on how different techniques can be used in detection of fraud crime in different types of business transaction. Moreover, it highlighted the shortcoming on the different techniques. This is crucial since it provided 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 privacy concern. This was crucial since it gave insights on how the business world can continue using data mining techniques to combat crime without risking loss of reputation. Moreover, the companies can use data mining for fraud detection crimes while making less error such as those of biasness (Clifton Web). Conclusion Data mining has undergone modification with technological advancement. Data mining play a great role in alter detection of problems such as frauds.This is because it enables 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 decision tree, the author group data mining based on the techniques used. By using clustering, the data was grouped to obtain certain patterns. The analysis was important to business world since it provided insights on how the different data mining techniques work. Works Cited Clifton, Christopher. data mining . Encyclopedia Bratanicca (n. d. ) 1-3. Web. .