T
he effectiveness of data mining is realized when users can get the best out of their databases. Reliability and accuracy are very important in statics because they contribute to the applicability of the conclusions made. The use of clustering, classification, and correlation helps to maximize the benefits derived from data sets stored in a database.
Clustering
Clustering is the process of classifying data into useful sub-classes. The information stored in a database is of varied nature and characteristics. During data mining, the data sets are grouped according to their similar attributes. Clustering helps in grouping the data sets into different demographic. For instance, for effective planning, a reader may want to classify information into various categories. This process is possible through clustering.
Association and Correlation
Correlation helps to establish relationship between two or more variables. The reader should choose a data mining software that is programmed with algorithms that create accurate connections between the defined variables. For instance, the analytics component of the software should easily establish an association between age and preference. Consequently, the data set obtained is accurate and reliable.
Classification
Classification is aided by machine learning. This data mining technique categorizes each item in the data set into one of the preferred groups. As well as statistics, classification uses decision tree to arrive at the final decision. A reader does not have all the time to go through the data set without a clear direction. He or she needs assistance in creating classes for ease of analysis. Through classification, his or her work is made easier and the required data is extracted within the shortest time.
