Data mining is on high demand nowadays due to quite many reasons. First, a very promptly changing business environment persuades us to be fast enough in response, which is followed by decision making. To react to those rapid changes intuitively is rather risky than responsible. Even a tactical step in business must be based on analytical conclusions. Therefore, we just cannot deny the fact of data actuality.
Statistically, data can be divided into quantitative (numerical) and qualitative (categorical) data. Moreover, it can be categorized as primary source data and secondary source data.
Prior to any search, you must ask yourself the basic initial questions:
- What is the purpose of the analysis?
- What is the main question you do ask prior to your analysis?
- Where potentially possible to find essential data?
- Is the data to be used for the descriptive, inferential or predictive analysis?
Data mining attempts to discover patterns, trends, and relationships among data, especially non-obvious and unexpected patterns. A collection of methodologies is usually used by analysts for that purpose:
- Classification analysis attempts to find variables that are related to a categorical (often binary) variable.
- Prediction tries to find variables that help explain a continuous variable, rather than a categorical variable.
- Cluster analysis tries to group observations into clusters so that observations within a cluster are alike, and observations in different clusters are not alike.
- Market basket analysis tries to find products that customers purchase together in the same “market basket.”
- Forecasting used to predict values of a time series variable by extrapolating patterns seen in historical data into the future.
Data mining is a relatively new field. Experts still discuss a proper definition for the process. Nevertheless, it can be generally defined as:
The process of data search, which includes advanced algorithms that can be used to find useful information and patterns in large data sets.
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