Data mining is the process of determining patterns to create insights and solutions for organizations.
Although largely used for organizational purposes, some individuals have been utilizing their knowledge in data mining to unlock incoming game updates prior to its public release.
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Regardless, data mining has proven to be one of the most efficient technological skills that one should learn.
Whether your just curious or want to do it yourself, here are some data mining techniques that you should know.
Classification
Classification involves handling and categorizing various attributes associated with different type of data. This process requires building a predictive model through a labeled dataset where class labels are identified.
Algorithms such as Decision Trees, Naive Bayes, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN), fit the classification technique.
Clustering
This technique promotes visual strategy to understand the data. For instance, graphics are commonly used to show the distribution and relationship of each data.
Some of the clustering algorithms are k-Means, Hierarchical Clustering, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN).
Association
Association technique is associated to statistics, wherein the main goal is to find co-occurrence patterns.
The data output is shown in the form of if-then statements. Apriori and FP-Growth are widely used algorithms for association rule mining.
Regression
This technique aims to identify the nature of the relationship of the variables. In addition, regression is mostly used for forecast and data modeling.
Linear Regression, Logistic Regression, and Polynomial Regression are some of the regression techniques.
Outlier Detection
Outliers are the anomalies found within datasets. These are easily detected when there is a sudden spike or down within the data.
Popular methods for anomaly detection include Isolation Forest, One-Class SVM, and Local Outlier Factor (LOF).
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