What are the key concepts of data mining?

In our previous blog, we wrote about data mining and its impact on the decision making of many companies.

Getting the best results from data mining requires a variety of tools and techniques. But, to have an in depth understanding of the process, it is necessary to know the key concepts:

  • Cleaning and preparing data: Data is transformed into a suitable format for later analysis and processing (such as the identification and elimination of errors and missing data).
  • Artificial Intelligence (AI): These systems perform analytical activities associated with human intelligence (such as planning, learning, reasoning, and problem solving).
  • Association rules learning: These tools are also known as market basket analysis and are responsible for looking into relationships between variables in a data set, such as determining which products are normally bought together.
  • Grouping: Is the process of partitioning a data set into a set of meaningful subclasses (called clusters), with the purpose to help users understand the natural structure or grouping of the data.
  • Classification: This technique assigns elements in a data set to categories or target classes in order to accurately predict each case in the data.
  • Data analysis: In this process digital information is evaluated into useful business intelligence.
  • Data warehousing: Is a large collection of business data that is used to help an organization make decisions. It is the fundamental component of most large-scale data mining efforts.
  • Machine Learning: This computer programming technique uses statistical probabilities to give computers the ability to “learn” without being explicitly programmed.
  • Regression: A technique used to predict a range of numerical values, such as sales, temperatures, or stock prices, based on a particular data set.
How does data mining work?

A typical data mining project begins with asking the right business question, collecting the right data to answer, and preparing the data for analysis. Success in the later stages depends on what happens in the early stages.

Data mining professionals generally achieve reliable and timely results by following a structured process that involves these six steps: 

  1. Understanding: Developing an in-depth understanding of the project parameters, including the current business situation, the project’s primary business objective, and the criteria for success.
  2. Data understanding: Determine the data that will be needed to solve the problem and collect it from all available sources.
  3. Data Preparation: Preparing the data in the appropriate format to answer the business question, solving any data quality problems, such as missing or duplicate data.
  4. Modeling: Use of algorithms to identify patterns within the data.
  5. Evaluation: Determine if, and to what extent, the results delivered by a given model will help achieve the business objective. 
  6. Implementation: Make project results available to decision makers.

Why is data mining so important to us? Thanks to our proprietary technology at LISA Insurtech we are able to empower the insurance industry by detecting fraud, segmenting the preference of policyholders, and much more. 

If you want to know how we work, click here 

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