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Methods For Clustering with Constraints in Data Mining

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  • Last Updated : 11 May, 2022
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Data mining is also called discovering the knowledge in data, basically, it is the process of uncovering the various patterns and valuable information from given large data. Data mining has a large impact on organizations as it improves organizational decision thinking and making through data analyses. Data mining is mainly divided into various steps such as from data collection to visualization to the last part where we extract very valuable information regarding our data.

In this article, we know about methods for clustering with constraints in data mining.

A cluster is also known as a subset of similar known objects. The distance between any two objects in the cluster is always less than the distance between the two objects in the cluster.

Clustering in Data Mining:

Clustering is the most important type of process in data mining. The main work of clustering is converting a group of abstract or different objects into similar objects. It is also used for separating the data or objects into a set of data or objects which finally gets into a group of subclass called a cluster. Various data objects in a cluster are considered as one single group. We firstly divide the given information into groups and all similar data are assigned to one group.

Why do We Use Clustering in Data Mining:

Clustering is used in data mining for various reasons:

  • Scalability: Scalability in the clustering process terminates the process that if we increase the number of data objects, the time to complete clustering is nearly scaled to complexity order in the algorithm.
  • Interpretability: The output of this clustering process should be interpretable and should be used so that it can be used efficiently.
  • Easy to Handle Noisy Data: It has the ability to deal with noisy data present in a database that is incorrect or missing.
  • Able to Deal With Various Attributes: It can deal with different types of attributes and it can be applied to any data such as data based on binary form, or numerical form.
  • High Dimensionality: The clustering process can handle any type of data. It can handle even high-dimensional data and low-dimensional data space.

Constrained Clustering:

Constrained clustering is an approach to clustering the data while it incorporates the domain knowledge in form of constraints. All data including input data, constraints, and domain knowledge are processed in the clustering process with constraints and give the output clusters as an output.

Constrained Clustering

 

Methods For Clustering With Constraints:

There are various methods for clustering with constraints and can handle specific constraints:

  • Handling Hard Constraints: There is a method for handling the hard constraints by regarding the constraint in a cluster assignment procedure. It is a very important method for handling the difficult constraints we can regard the constraints in the assignment procedure of cluster.
  • Generating the super instances for must-link constraints: There are must-link constraints that have transitive closure that can be calculated by it. so that we can say that must-link constraints are known as an equivalence relation. The subset can be defined by it. In subset, there are some objects which can be replaced by the mean.
  • Handling the soft constraints: In the clustering process of soft constraints, there is always an optimization process. There is always a penalty requires in the clustering process. Hence the optimization in this process’s aim is to optimize the constraint violation and decreasing the clustering aspect. For example, if we take two sets one is of data sets and the other is a set of constraints, CVQE stands for Constrained Vector Quantization Error. In the CVQE algorithm, K-means clustering is enforced to constraint violation penalty. The main objective of CVQE is the total distance used for K-means which are used as follows:
    • Penalty in must-link violation: This penalty occurs due to when there is a must-link constraint present on objects y, x. They are created to the given two centers C1, C2 by which the constraint can be violated hence the distance that lies between C1 and C2 is inserted but as a penalty.
    • Penalty in cannot-link violation: This type of penalty is different from a must-link violation as in this penalty there is one center created to a common center C when cannot link is present on objects x, y. Therefore the constraints are violated and hence the distance that lies between (C, C) can be inserted in the objective function and it is recognized as a penalty.
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