Here’s a similar structured description for **Clustering in Data Mining**:
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Clustering in Data Mining
Clustering: is a foundational technique in data mining that focuses on grouping data points into clusters based on their similarities. This method, often used in exploratory data anal...
Unit V - Clustering
• Cluster analysis, also known as clustering, is a method of data mining that groups similar data points to
goal of cluster analysis is to divide a dataset into groups (or clusters) such that the data points within each gro
similar to each other than to data points in other groups. This process is often used for exploratory data analysis
identify patterns or relationships within the data that may not be immediately obvious.
Clustering Methods:
1. Partitioning Method: It is used to make partitions on the data in order to form clusters. If “n” partition
on “p” objects of the database then each partition is represented by a cluster and n < p. The two conditions
to be satisfied with this Partitioning Clustering Method are:
• One objective should only belong to only one group.
• There should be no group without even a single purpose.
2. Hierarchical Method: In this method, a hierarchical decomposition of the given set of data objects are crea
Dr.Priya Govindarajan
,There are two types of approaches for the creation of hierarchical decomposition, they are:
•Agglomerative Approach: The agglomerative approach is also known as the bottom-up approach
given data is divided into which objects form separate groups. Thereafter it keeps on merging the o
groups that are close to one another which means that they exhibit similar properties. This mer
continues until the termination condition holds.
•Divisive Approach: The divisive approach is also known as the top-down approach. In this approac
start with the data objects that are in the same cluster. The group of individual clusters is divide
clusters by continuous iteration. The iteration continues until the condition of termination is met or unti
contains one object.
3. Density-Based Method: The density-based method mainly focuses on density. In this method, the
will keep on growing continuously as long as the density in the neighbourhood exceeds some threshold
data point within a given cluster. The radius of a given cluster has to contain at least a minimum numb
4.Grid-Based Method: In the Grid-Based method a grid is formed using the object together,i.e, the ob
quantized into a finite number of cells that form a grid structure.
Dr.Priya Govindarajan
, 5. Model-Based Method: In the model-based method, all the clusters are hypothesized in order to find the data
suited for the model. The clustering of the density function is used to locate the clusters for a given model. It
spatial distribution of data points and also provides a way to automatically determine the number of clust
standard statistics, taking outlier or noise into account. Therefore it yields robust clustering methods.
6.Constraint-Based Method: The constraint-based clustering method is performed by the incorporation of a
user-oriented constraints. A constraint refers to the user expectation or the properties of the desire
results. Constraints provide us with an interactive way of communication with the clustering process. The
application requirement can specify constraints.
Applications Of Cluster Analysis:
• It is widely used in image processing, data analysis, and pattern recognition.
• It helps marketers to find the distinct groups in their customer base and they can characterize their customer gro
purchasing patterns.
• It can be used in the field of biology, by deriving animal and plant taxonomies and identifying genes w
capabilities.
• It also helps in information discovery by classifying documents on the web.
Dr.Priya Govindarajan
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