24-11-2017, 10:29 AM
Data mining is the process of classifying large data sets to identify patterns and establish relationships to solve problems through data analysis. Data mining tools allow companies to predict future trends. In data mining, association rules are created by analyzing the data for frequent if / then patterns, then using the support and confidence criteria to locate the most important relationships within the data. Support is the frequency with which the items appear in the database, while trust is the number of times the statements are accurate.
Other data mining parameters include Sequence or Path Analysis, Classification, Clustering and Forecasting. Sequence or route analysis parameters look for patterns in which one event leads to another event later. A sequence is an ordered list of sets of elements, and is a common type of data structure found in many databases. A classification parameter looks for new patterns and can generate a change in the organization of the data. Classification algorithms predict variables based on other factors within the database.
The grouping parameters find and visually document groups of events that were previously unknown. The grouping groups a set of objects and adds them according to their similarity. There are different ways in which a user can implement the cluster, which differentiate between each grouping model. The promotion of parameters within data mining can uncover patterns in the data that can lead to reasonable predictions about the future, also known as predictive analysis.