The cluster analysis, a valuable tool, assists researchers in determining varying patterns from unstructured data to give them meaning. When meaningful themes emerge from a large dataset, decision-making becomes simplistic and easier for its users. This article will discuss cluster analysis and its application in various practical fields with examples.
What Is A Cluster Analysis Example?
Cluster analysis is a data-mining tool that identifies distinct and discrete groups or objects. These groups or objects can be sale transactions, behavior’s or characteristics. The aim behind cluster analysis is to search for a similar group that possesses similar characteristics and features. For instance, universities use cluster analysis to identify high achievers of the overall session to announce scholarships on merit. Academicians denote cluster analysis as segmentation or taxonomy analysis.
Types of Cluster Analysis
Following are some types of cluster analysis that researchers opt to plot and handle complex datasets;
Hierarchical Cluster Analysis
This cluster analysis type creates a series of predetermined clusters from top to bottom. For instance, arranging and organising files from top important to least important in a rack. Hierarchical cluster analysis efficiently handles the nominal, ordinal and scale data of the variables.
Two-Step Cluster Analysis
This method is referred to as two-step as it first identifies groups by cluster analysis. After groupings from cluster analysis, it then uses a hierarchical approach to handle categorical or continuous variables. This cluster analysis type involves the researcher’s exploration and automation to outline natural groupings from a data set. It handles ordinal and scale data of the variables.
K- Means Cluster Analysis
This cluster analysis type makes groupings of large datasets and variables that researchers fail to label. It helps researchers in identifying unknown groups that exist in a complex dataset. However, it is difficult for most of researchers therefore, thety prefer to hire masters dissertation writing services.
Applications of Cluster Analysis
The common applications of cluster analysis in the practical field of life are as follows;
Science
In the field of science, cluster analysis has wide applications in biology, clinical medicine, and epidemiology. Biologists use this tool for taxonomy. Clinical medicine uses it to determine the number of patients having a similar disease. It outlines patients receiving similar treatment with a similar response towards the treatment. Epidemiologists use it to outline different regions, communities or groups with similar epidemiological profiles.
Statistics
Statisticians use cluster analysis to categorise and plot numeric data into meaningful information. The information is then presented to economists that help in their decision-making.
Geology
Geologists use cluster analysis to determine the vulnerability of certain areas for natural disasters. For instance, geologists use information derived from cluster analysis to identify the seismic risk cities and the areas more open for earthquakes and volcano eruption to minimise the risk for damage.
Insurance
The insurance domain relies on the information of the cluster analysis in its decision-making. For instance, if a certain region is demanding more claims, the cluster analysis will assist insurance agents in identifying “why”. It means cluster analysis will research the issue and explore why certain regions demand more claims. The information is then used to either allow or reject applications of claims.
Marketing
The field of marketing use cluster analysis mainly for two purposes. One purpose involves exploring markets and identifying suitable market segments of brand offerings. The other purpose involves positioning market offerings to satisfy the unmet needs of consumers. Hence, marketers use cluster analysis to segment and position market offerings to existing and new markets.
What is Cluster Analysis in Statistics?
Statisticians sort out complex data by using an inductive, exploratory technique to give it meaning, called cluster analysis. In statistics, cluster analysis is a predefined set of tools and algorithms that generates hypothesis. The cluster analysis assists statisticians in categorising distinct objects and groups in a way that identifies their similarities and rejects those with dissimilarities.
It does not indulge statisticians in defining the reasons for the existence of the datasets. But it allows statisticians to uncover groups that have maximal similarities. The underlying purpose of using cluster analysis in statistics is to achieve an optimal solution. This optimal solution outlines the similarities and dissimilarities across clusters in the best manner.
Doing Cluster Analysis in Statistics
Statisticians can opt for the following steps in conducting a comprehensive cluster analysis;
Step 1: Selecting Groups for Cluster Analysis
Statisticians should establish grounds for similar clustering groups. The grounds can either be scales or variables.
Step 2: Preliminary Data Processing
In this step, preliminary data processing should be done before clustering similar groups. It means statisticians have to overcome the missing values in a given data set either through imputation or deletion. Moreover, they have to address the normality distribution of the data. The underlying aim behind preliminary data processing is to identify gaps and weaknesses in a given dataset. The statistician can even involve standardising variables set and addressing their outliers. Hence, the statistician can evenly distribute data with these data processing techniques.
Step 3: Identify Type of Cluster Analysis
Statisticians have to identify the most appropriate type for cluster analysis for their data set. For instance, statisticians can use hierarchical cluster analysis to identify from the most important to the least important. On the other hand, if researchers need efficient and less time-consuming types to categorise similar groups, they should use two-step clustering.
Step 4: Assess the Validity of Cluster Analysis
In this final step, statisticians have to assess the validity of the cluster analysis. The validity can be assessed by two methods. One method entails indexes for internal evaluation. The second method involves measuring differences among subgroups. Both methods determine the validity of the cluster analysis that assists statisticians in generalising the study’s findings.
Final thoughts
Cluster analysis is applicable in almost every field of life that requires grouping or categorising complex data into simplistic forms to ease the decision-making process. It involves hierarchical, two-step, and K-means cluster analysis that efficiently handles large datasets. So, cluster analysis benefits its users by making similar population groups, objects and subjects.
