DATA MINING

Data mining is processing data in order to identify patterns and trends that can be used to make decisions or judgments. The process of data analysis, discovery, and mining-model building is often iterative. Different sources and tools provide flexibility in techniques and a variety of results. Although terminology is inconsistent throughout the industry, this article considers eight different mining techniques as examples.
 
Association is a simple correlation between two or more items. Classification describes a type of data source using multiple attributes. Clustering uses one or more attributes for identifying correlating results. Prediction, used in combination with the other data mining techniques, involves analyzing trends, classification, pattern matching, and relation to determine the likelihood of an event. Sequential patterns, normally analyzed over a long term, are used to help identify trends, or regular occurrences of similar events. Decision trees use questions and answers along with other techniques to help classify or identify data. Combinations of techniques is more common than the application of a single technique. Long term (memory) processing is keeping a record of the information and learning from it.
 
Preparing a suitable data model and structure allows for efficient data mining. SQL is commonly used to communicate with a database. According to ANSI (American National Standards Institute), it is the standard programming language for relational database management systems. Concepts used to design in SQL translate to any data query. Examples include structuring and formatting the information so that it can identify sequences and relationships, as well as using source data to build new inquiry points. Finally, reduction can be used to summarize or quantify the information and then output that information in a standardized structure based upon the totals, sums, statistics or other analysis selected for output.
 
REFERENCE

​Brown, M. (2012). Data Mining Techniques. IBM. Retrieved from https://www.ibm.com/developerworks/library/ba-data-mining-techniques

INSIGHT
 
Applying the concepts in this article starts by changing the words “business” and “customer” to “education” and “student,” and then considering the ideas in a general fashion. Begin by realizing that even though big data refers to extremely large groups of people, these mining concepts can be applied when analyzing any data. Then consider the Common Core curriculum where standards do not predicate consistent content types or delivery of information. Because students have different learning styles, these variations can have significant impacts on an individual’s ability to learn the topic. These inconsistencies may be even more apparent when considering emotional intelligence in relation to the material. Gaining insight into these dynamics through systematic assessment of students, teaching styles, and curriculum choices; and mining the data, could reveal trends that can be followed or adaptations that could be implemented which may help increase assimilation of material.