Assignment task: Cite and reference the following:
The main data-mining methods.
Data mining is the process of successfully discovering new information (that did not exist before) by studying large data sets. However, data mining is not about extracting new data but inferring patterns from previously collected data. Skills and techniques for mining data may be based on machine learning, statistics, and database management, and professional data miners strive to draw accurate conclusions from large amounts of data and information.
Explain the fundamental differences between the data-mining methods.
Association- An association may also be a tracking pattern, but the dependability of the variables is studied closely here. Two events or two attributes may be closely linked to each other or may correlate with one another to a greater extent.
Regression analyses- Regression analyses the likelihood of a given variable in the presence of another variable. Regression provides for a better analysis of the different variables and their interrelationship.
Tracking patterns- Tracking patterns in a given data set is one of the most basic data mining processes. The data miners may look for any aberration in the data at more regular intervals or find out the flow or ebb of a variable.
Classification- The classification of data may be more complex. The data miners create categories based on perception and collect specific attributes together. Classification provides for better analysis or helps in making accurate conclusions.
Outlier Detection- Sometimes, the mere knowledge of a given pattern (as in pattern tracking) may not provide enough insights, and the outliers or anomalies also need consideration and analysis.
Clustering- While classification is based on any attribute or aspect, data clustering is based on grouping data and items based on their similarities.
Prediction- One can use Data mining methods or techniques to forecast the kinds of data or the trends that will be available.