Data Mining

Data Mining

Clustering

This technique is used in data mining to divide data sets into groups that contain the same objects, plus they give the end-user to have a high level of viewing the database (Srinivas at el 2018). The data is collected and sampled so that it can display the same meaning. When the cluster is put in the same data, set it helps the user to relocate the information that he wants from the database easily. Clustering also helps the user to have the understanding of having groupings of data and the structure of the data set. The clustering helps to have predefined classes that will help the user to search the data in the database easily and have a clear display of having the content that he is relocating to the database.

Classification

The classification is applied in data that is being held aside so that it can be compared to the prediction of the target values. Rules are set that will help answer the questions that will help predict the outcome in the database (Aggarwal at el 2018). The training data is set, and it has to contain attributes that are likely to be outcomes. Then the classification algorithm has to discover the way the attributes help the user to have the conclusion of his search..

Association rule mining

This technique helps to know the relation of data that we have in the database and how it will have a correlation with the data we are searching through the search engine (Prajapati at el 2017). The algorithm is set in a way that will display data from the data sets that have similarities at the viewpoint of the user. Thus becomes a powerful tool in data mining.

Anomaly detection

Data mining uses this technique to detect behaviors that are sudden to the system, plus it helps to detect intrusions into the database, and they have the attribute of sending the notification when they have detected the behavior (Agrawal at el 2015). Thus this is an attack control mechanism that is used to protect the data warehouse form external attacks.

Reference

Aggarwal, C. C., & Reddy, C. K. (2018). Data clustering: algorithms and applications. Chapman and Hall/CRC.

Agrawal, S., & Agrawal, J. (2015). Survey on anomaly detection using data mining techniques. Procedia Computer Science, 60, 708-713.

Prajapati, D. J., Garg, S., & Chauhan, N. C. (2017). Interesting association rule mining with consistent and inconsistent rule detection from big sales data in a distributed environment. Future Computing and Informatics Journal, 2(1), 19-30.

Srinivas, B., Ramesh, G., & Sriramoju, S. B. (2018). An Overview of Classification Rule and Association Rule Mining. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 3(1), 643-650.