Data Mining Methods

 

As I have discussed throughout this blog, the among of data generated today and the rate that it is generated and mind boggling. Data mining is the process through which value is extracted from this endless sea of data. It involves sifting through massive datasets to uncover anomalies, patterns, and correlations that can help solve problems through data analysis. This process relies heavily on the effective implementation of data collection, warehousing, and processing. This process is only becoming more valuable with the growth of Big Data and data warehousing. (1)

There are several powerful techniques that make up the process of effective data mining:

Association Rules – This technique involves searching for relationships between variables. This relationship creates additional value within the data.

Classification – This technique involves assigning objects to predefined classes. This groups objects by characteristic or some other common factor. This helps organise and summarize data making it more legible.

Clustering – Similar to classification this technique identifies similarities between objects but then groups those objects based on their differences to other items. This leads to broader groups which can be more beneficial that classification.

Decision Trees – This technique involves predicting outcomes based on a set list of criteria or decisions. The decision tree is a series of cascading questions that sort data based on the responses. This allows for user interaction with the data set.

K-Nearest Neighbour – This technique classifies data based on the data’s proximity to other data. This creates groups by assuming a similarity between data points close to one another.

Neural Networks – This technique is modelled on human learning and processes data through a series of nodes. These nodes are comprised of inputs, weights, and an output. These advanced techniques once trained can then process new data similar to old data all on their own.

Predictive analysis – This technique uses historical information to build mathematical models to predict future outcomes. Using vats quantities of data this technique can help businesses and organisations plan for the future. (2)

 

https://www.techtarget.com/searchbusinessanalytics/definition/data-mining#:~:text=Data%20mining%20is%20the%20process,make%20more%20informed%20business%20decisions.

https://www.investopedia.com/terms/d/datamining.asp

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