Content Writer
Cluster Analysis is a statistical paradigm of data analysis that is used in classification of disorganized components into groups or clusters that possess similar characteristics. It is a difficult task to group individual objects based on their similarities. Hence, this process employs various algorithms and techniques to collect cluster data and group them into particular clusters.
For example, if aquatic ecosystems in the ocean are considered as the data set. This dataset can be broadly classified into clusters as fishes, crabs, lobsters, whales, sharks and dolphins.
The main objective of the cluster analysis is to find similar groups of objects where each pair of object similarity represents some overall measures throughout the entire spectrum of qualities.
Read more: Statistics
| Table of Content |
KeyTerms: Cluster analysis, Statistics, Data analyzing, Data mining, Hierarchical cluster analysis, Centroid-based clustering, Cluster similarities
What is Cluster Analysis?
[Click Here for Sample Questions]
Cluster Analysis is the data analyzing technique that aims to group things (such as goods, individuals or other entities) based on a set of user-selected traits or features. It is also crucial that clusters represent high internal homogeneity and high external heterogeneity. This fundamental and vital step of data mining, which is also a popular technique for statistical data analysis, is used in data compression, machine learning, pattern recognition, information retrieval, and other fields.
Cluster analysis of objects
Read more:
| Relevant Concepts | ||
|---|---|---|
| Data Collection and Organization | Types of data analytics | Set Operations |
| Application of linear graphs | Linear functions | Mean Theorem |
Types of Cluster Analysis
[Click Here for Sample Questions]
The Cluster Analysis algorithm is specific to a particular data set. Some of the types of cluster analysis are as follows:
Hierarchical Cluster Analysis
This approach involves creating a cluster first, then joining it to another cluster (the one that is most comparable and nearby) to create a single cluster. Until all of the subjects are grouped together, this process is repeated. Agglomerative method is the name given to this particular approach. Agglomerative clustering begins with a single object and clusters it into groups.
The divisive method is a different form of hierarchical clustering technique that begins by partitioning the entire data set.
Centroid-based Cluster Analysis
This type of clustering uses a central element that may or may not be a part of the given data set to describe clusters. In this method, objects are assigned to the closest cluster centers using the K-Means method of clustering, where k are the cluster centers.
Centroid-based clustering
Partitioning Cluster Analysis
In this approach, each data member is first aggregated, and after a number of continuous iterations, they finally separate.
Density-Based Cluster Analysis
This approach is focused on the density of the data members. With at least one member in the group's radius, the cluster expands continually.
Density-based clustering
Grid-Based Cluster Analysis
The quantization of the object space into a finite number of cells creates a structure that resembles a grid.
Model-Based Cluster Analysis
Every cluster is hypothesized in this case to discover the data that could accommodate the model the best.
Constraint-Based Analysis
Data clustering is carried through using application- or user-oriented constraints.
Types of Data in Cluster Analysis
[Click Here for Sample Questions]
A dataset intended for cluster analysis must have data in a specific format. The types of data in cluster analysis is mentioned below:
| Types | Explanation |
|---|---|
| Interval-Scaled Variables | They are linear scale with continuous measurements |
| Binary Variables | These variables only pertain to 2 values |
| Nominal Variables | These are similar to binary variables but can pertain more than 2 values |
| Ordinal Variables | These can be either continuous or discrete |
| Ratio-Scaled Variables | On a non-linear scale, these variables have a positive value that is approximately exponential. |
| Variables of Mixed Type | All six types of variables—symmetric and asymmetric binary, ordinal, interval, nominal, and ratio—could be represented in these datasets. |
Examples of Cluster Analysis
[Click Here for Sample Questions]
Some of examples of the cluster analysis are given below:
- Marketers utilize cluster analysis in order to find customer base and information to facilitate targeted marketing programs.
- Used to analyze the pets of people in targeted cities.
- Cluster analysis helps in city-planning based on the house value, geographic location and other parameters.
- It also helps in visualizing the land used on earth with observation data.
Read more:
| Relevant Concepts | ||
|---|---|---|
| Venn Diagram | Conditional Probability | Matrices |
| Continuous variable | Direct Variation | Identity Matrix |
Applications of Cluster Analysis
[Click Here for Sample Questions]
Cluster Analysis plays a significant role in data analysis. Here are few applications of cluster analysis:
- It is the main objective of exploratory data mining, which is a typical technique for statistical data analysis.
- It is utilized in a wide range of industries, including computer graphics, bioinformatics, image analysis, pattern recognition, information retrieval, and machine learning.
- It can be used to analyze drugs based on their antibacterial action, study patterns of antibiotic resistance, and include antimicrobial substances based on their mechanism of action.
- Cluster analysis is employed in studying and observing earthquakes.
Things to remember
- Cluster analysis is statistical paradigm used to organize group of objects into clusters based on their similarities,
- Cluster analysis is primarily employed for data mining.
- It algorithm obtained are specific to particular cluster and is irrelevant to other cluster
- Agglomerative clustering begins by collecting individual objects into clusters.
- Clusters must represent high internal homogeneity and high external heterogeneity.
- The variables of mixed type contain all six types of variables - symmetric and asymmetric binary, ordinal interval, nominal and ratio.
Sample Questions
Ques. What is Cluster Analysis? (1 mark)
Ans. The methodology known as cluster analysis groups data objects based on the data set's descriptions of the objects and their characteristics.
Ques. What is the main objective of cluster analysis? (1 mark)
Ans. The main objective of cluster analysis is to group objects into clusters with similar characteristics.
Ques. What are the two types of Clustering? (2 marks)
Ans. The two types of cluster analysis are:
- Hierarchical clustering: Clusters are organized in hierarchical tree structure
- Partitioning clustering: Data are split into various, non-overlapping subsets.
Ques. Describe the k-Means Clustering. (2 marks)
Ans. Each cluster in the partitioning clustering method known as K-Means clustering has a centroid or center point, and each data point is given the centroid that is nearest to it. The total number of clusters is already known.
Ques. How is the quality of a cluster measured? (3 marks)
Ans. The quality of a cluster is measured by
- Size of the cluster in relation to the distance between clusters
- The distance between cluster members
- The smallest sphere's diameter
Ques. Mention two limitations of k-Means clustering (2 marks)
Ans. The limitations of k-Means clustering are as follows:
- Ineffective if there is outliers in the data
- fails for circular non-convex clusters
Ques. What is a Dendrogram? (1 mark)
Ans. A dendrogram is a type of tree diagram used to demonstrate the way clusters are arranged in a hierarchical clustering.
Ques. How is the centroid for the cluster selected ? (4 marks)
Ans. The cluster centroid is selected as follows:
- The feature space is randomly selected.
- Randomly selecting a sample of the data
- Identify locations of space that are crowded
- Distribute them evenly across the feature area.
Ques. Is a cluster analysis qualitative or quantitative (1 mark)
Ans. By using a quantitative tool to assess data produced by qualitative research, cluster analysis enables the blending of methods.
Ques. Mention the types of data (3 marks)
Ans. The types of data in cluster analysis are as follows:
- Interval-Scaled variables
- Binary variables
- Nominal variables
- Ordinal variables
- Ratio-scaled variables
- Variables of mixed-type
Check out:
Comments