Data mining techniques

10 of the Most Important Data Mining Techniques

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In this post, we’ll cover some of the most important data mining techniques, strategies, and processes. 

First, though, let’s see where data mining fits into the overall data processing pipeline.


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Where Data Mining Fits into the Data Pipeline

A data pipeline is a process by which data is extracted from one source, processed, and new data is produced at the other end.

One example is the ETL pipeline, which stands for:

  • Extract the data
  • Transform the data
  • Load the data into the destination

In addition to ETF, there are several other data pipeline architectures.

The Cross-industry standard process for data mining (CISP-DM), for instance, is a common approach to data mining that includes six main stages:

  •     Business Understanding
  •     Data Understanding
  •     Data Preparation
  •     Modeling
  •     Evaluation
  •     Deployment

Generally, these stages are followed in sequence, though data scientists can move forward or backward as necessary.

The ETF and CISP-DM models are only two examples of a pipeline architecture. Pipelines may be built in different ways. 

A batch-based pipeline, for example, would push a large data set through a pipeline and perform operations on it until it arrives at the data warehouse. A streaming data pipeline, on the other hand, would continuously feed data through the pipeline, while performing operations on it at the same time.

At each stage of any data pipeline, different operations are performed, including steps such as data extraction and data mining. Below, will look at some techniques that are used during the mining stage.

10 of the Most Important Data Mining Techniques

Data mining refers to the analysis of data to gather insights.

There are several methods that can be used to gather insights into data including:

1. Data preparation

Data must be prepared and “cleaned” before it can be analyzed. This means removing errors, irrelevant data, duplicate data, and so forth. 

It is also necessary to structure data properly so that it may be analyzed and mined.

2. Tracking patterns

Tracking patterns, unsurprisingly, means discovering patterns within the data and monitoring those. Those patterns, in turn, can be the starting point for deeper insights.

3. Classification

Classification categorizes data into segments or subtypes. Marketers, for example, may classify customers by demographics, income, location, and so forth.

4. Regression

Regression is a statistical method, much like many of the other methods covered here.

The goal is often two predict the future of, for example, a trend, a variable, a series of events, and so forth.

5. Clustering

Another statistical method, clustering groups data together based on similarities.

6. Association

Association is designed to connect specific variables, such as the frequency of a purchase. It is similar to pattern tracking but focuses only on dependently connected variables.

7. Prediction

Prediction is used heavily in areas such as predictive analytics. Like regression it can be used to predict future trends or events.

8. Decision trees

A decision tree begins with a variable and then moves down a series of decisions. Each decision is like a branch, with multiple options. The decision tree will progress through each branch until it comes to a resolution about the data in question.

9. Outlier analysis

Detecting outliers refers to the detection of anomalies, or data points that are far outside the norm. This can identify certain trends or patterns That are outside the norm, which can be used to improve decision-making.

10. Visualization

All of the techniques covered above refer to statistical techniques used in data science. Visualization, on the other hand, refers to translating that data into a graphical representation. This is important because humans need visuals to gain a quick grasp of data insights and patterns.

Data Mining in Practice

What are the benefits of data mining?

Data mining and related data-driven techniques can be used in a wide range of activities, including:

  • Customer success and the customer experience
  • Marketing and sales
  • Banking and finance
  • Scientific research
  • Organizational change management
  • Healthcare
  • Business intelligence

Effective use of data mining in these areas can generate insights that drive number of business benefits, such as:

  • Enhanced decision making
  • Improved business outcomes
  • Increased profitability
  • Deeper insights into business initiatives
  • Greater business process efficiency
  • Fewer errors and in accuracies

All of these benefits, in turn, can be used with more modern techniques to not only boost operational efficiency, but to fuel data-driven digital transformation.

The Future of Data Mining

Data mining is a core step in any data pipeline, as we have seen. It is a standard technique within data science fields that can add value to any business.

However, as with any other business field or scientific discipline, data mining and data science disciplines are evolving rapidly.

Today, for instance, data mining is being used at a very high level in organizations that want to improve business processes – unsurprisingly, this technique is called process mining.

Process mining has become a popular discipline among large corporations that want to mine, enhance, and transform business processes.As technology and data science continues to change, in the years ahead we can expect to see newer techniques enter the business world and transform the way organizations operate.

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