Data Mining Techniques

Data Mining Techniques

  • Data mining techniques includes the use of refined data analysis tools to seek out previously unknown, valid patterns and relationships in huge data sets.
  •  Data mining techniques
  • These tools can incorporate statistical models, machine learning techniques, and mathematical algorithms, like neural networks or decision trees.
  • Depending on various methods and technologies from the intersection of database management, machine learning , and professionals in data mining , statistics.
  • Major data mining techniques have been developed and used, including association, classification, clustering, prediction, sequential patterns, and regression.
 Data mining techniques

Data Mining Techniques


  • Classification data mining techniques involve analyzing the varied attributes related to differing types of data. Once organizations identify the most characteristics of those data types, organizations can categorize or classify related data.
  • It can be classified by different criteria, as follows:
    • Classification of Data mining frameworks as per the type of data sources mined:
      • It is as per the type of data handled. For example: multimedia, spatial data, World Wide Web, etc...
    • Classification of data mining frameworks as per the database involved:
      • It is based on the data model involved. For example: Object-oriented database, relational database, etc..
    • Classification of data mining frameworks as per the kind of knowledge discovered:
      • It depends on the types of knowledge discovered or data mining functionalities. For example: Discrimination, classification, clustering, and so on..
    • Classification of data mining frameworks according to data mining techniques used:
      • It is as per the data analysis approach utilized, such as neural networks, machine learning, visualization,..etc.
      • The classification can also take into account, the level of user interaction involved in the data mining procedure, such as query-driven systems, autonomous systems, or interactive exploratory systems.


  • Clustering may be a division of data into groups of connected objects. Describing the info by a couple of clusters mainly loses certain confine details, but accomplishes improvement. It models data by its clusters.
  • Data modeling puts clustering from a historical point of view rooted in statistics, mathematics, and numerical analysis. From a machine learning point of view, clusters relate to hidden patterns, the look for clusters is unsupervised learning, and therefore the subsequent framework represents a data concept.
  • From a practical point of view, clustering plays a unprecedented job in data processing applications.


  • Regression techniques are useful for identifying the character of the connection between variables during a dataset.
  • Those relationships might be causal in some instances, or simply correlate in others. Regression may be a straightforward white box technique that clearly reveals how variables are related.
  • Regression techniques are utilized in aspects of forecasting and data modeling.

Association Rules

  • Association may be a data processing technique associated with statistics. It indicates that certain data (or events found in data) are linked to other data or data-driven events.
  • It is almost like the notion of co-occurrence in machine learning, during which the likelihood of 1 data-driven event is indicated by the presence of another.
  • The statistical concept of correlation is additionally almost like the notion of association.
  • These are three major measurements technique:
    • Lift:
      • It measures the accuracy of the confidence over how often item B is purchased.
        • (Confidence) / (item B)/ (Entire dataset)
    • Support:
      • It measures how often multiple items are purchased and compared it to the overall dataset.
          (Item A + Item B) / (Entire dataset)
    • Confidence:
      • It measures how often item B is purchased when item A is purchased as well.
          (Item A + Item B)/ (Item A)

Outer Detection

  • This technique may be used in various domains like intrusion, detection, fraud detection, etc. It is also known as Outlier Analysis or Outilier mining.
  • It is a data point that diverges too much from the rest of the dataset.
  • The majority of the real-world datasets have an outlier. Outlier detection plays a significant role in the data mining field.
  • It is valuable in numerous fields like network interruption identification, credit or debit card fraud detection, detecting outlying in wireless sensor network data, etc.

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Sequential Patterns

  • Sequential pattern is a data mining technique specialized for evaluating sequential data to discover sequential patterns. It helps to discover or recognize similar patterns in transaction data over some time.


  • Prediction used a mixture of other data mining techniques like trends, clustering, classification, etc. It analyzes past events or instances within the right sequence to predict a future event.

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