Clustering in Data Mining
What is meant by Clustering in Data Mining?
Clustering in Data Mining can be defined as classifying or categorizing a group or set of different data objects as similar type of objects. One group or set refer to one cluster of data. Data sets are usually divided into different groups or categories in the cluster analysis, which is determined on the basis of similarity of the data in a group or a set. After the categorization of data into various groups or sets on the basis of their similarity, a label is assigned to each of those groups. This way it is very helpful for adapting to the changes by doing the classification or categorization on the basis of different criteria.
What is Cluster Analysis?
Now moving ahead, the next question is what is the meaning of Cluster Analysis in Data Mining?
The Cluster Analysis can be explained in many ways. In very simple words, it can be defined as the process of finding or determining the group of different objects that are either similar to each other in the group. However, at the same time, they are different from the objects in some other groups.
Applications of Cluster Analysis in Data Mining
- We usually see Cluster analysis being used, in general, in many applications. For example, market research, pattern recognition, data analysis, image processing and so on.
- With the help of Clustering, the dealer/seller can find or determine a set of groups in their customer base. After that, they can categorize those customer groups based on the trends or patterns observed in their usual purchases.
- We can use clustering even in the field of biology. We can use clustering to derive the various classification of plant and animal, also we can classify genes with similar functionalities. This way we can understand about different organisms in more detail.
- Clustering can also be used to help in categorizing documents on the web for finding various information.
- Clustering can be very often also used in outlier detection ways. To give some examples, the detection of credit card fraud is an outlier detection.
What are the benefits of using Cluster Analysis in Data Mining?
Cluster Analysis is very common and beneficial method in Data Mining. Following are some of the purposes that makes it so popular and helpful to the users.
- Interpretability
The outcome of cluster analysis should be readily available for use, easy to understand/comprehensible and capable of being explainable.
- Helps in dealing with unorganized data
When the data is messed up and unorganized, it cannot be analyzed fast enough, and this is one of the biggest reasons why there is a need to have Cluster Analysis in Data Mining. In clustering, with the help of “Grouping”, a user is able to organize the structure of the data with the help of putting the different sets of data into groups of the data objects that are similar to one another. This way, the data experts can easily categorize the messed-up data and hence, they can process the data in order to explore a few new things about the data.
- High Dimensional
Cluster Analysis in the Data Mining can also enable a user to handle the data of high dimension in company with the data of small size.
- Scalability
When the database is concerned, it is considered usually very enormous to deal with and for this purpose, the algorithm has to be scalable for the database.
Clustering Methods in Data Mining
We have different Clustering Methods in Data Mining. We can classify those into the different categories as listed below:
1. Partitioning
In this method, several partitions are created, after that those partitions are evaluated on the basis of some given criteria. To understand it more simply, let’s say there is a database which has ‘a’ and by using partition method, the ‘b’ partitions are constructed. A cluster will be depicted by each partition and the statement “b<a” will be true. Some requirements are also needed to be fulfilled which are:
- Each group has to contain at least one object.
- Each object has to belong to at least one group.
2. Hierarchical method
The second method in this list is Hierarchical Method, in this method, a set of objects of data is generated into a kind of hierarchical decomposition. This can be further classified into two different approaches which are:
- Agglomerative Approach: It is also known as the “bottom-up approach”, this approach mainly works on merging the objects or group that are somewhat similar to each other.
· Divisive Approach: It is also known as top-down approach, in this approach, the data expert will split up a cluster into smaller clusters until each one of the objects is in one cluster. The one thing a user needs to remember in this approach is that once merging or splitting is done, it can never be undone.
3. Density-based method
This method, as the name itself suggests, is based on density i.e., density reachability and density connectivity. To elaborate further, the plan behind this method is that the cluster continues growing until the density in the neighboring cluster exceeds some target.
4. Grid-based methods
In this method, the objects make a grid then that, grid structure is formed by quantifying the object space into a number of cells.
Advantages
- It is fast processing.
- It is only dependent on the number of cells present in each and every dimension.
5. Model-based methods
In this method, a model is formulated for each cluster so as to find the relevant data for a model. This method also gives the user an access to automatically determine the number of clusters on the basis of the standard statistics.
6. Constraint-Based Clustering Method
A constraint means the user expectation or the properties of desired clustering results. Constraints can be determined by the user or the application requirement.