WHAT IS THE PURPOSE OF CLUSTER ANALYSIS AND WHEN SHOULD IT BE USED INSTEAD OF FACTOR
ANALYSIS?
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances between cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including parameters such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It is often necessary to modify data preprocessing and model parameters until the result achieves the desired properties.
The purpose of cluster analysis is to place objects into groups, or clusters, suggested by the data, not defined a priori, such that objects in a given cluster tend to be similar to each other in some sense, and objects in different clusters tend to be dissimilar. You can also use cluster analysis to summarize data rather than to find "natural" or "real" clusters; this use of clustering is sometimes called dissection. The SAS/STAT procedures for clustering are oriented toward disjoint or hierarchical clusters from coordinate data, distance data, or a correlation or covariance matrix.
https://en.wikipedia.org/wiki/Cluster_analysis
https://support.sas.com/rnd/app/stat/procedures/ClusterAnalysis.html
Cluster Analysis (CA):
https://en.wikipedia.org/wiki/Cluster_analysis
is the most general way to group data in different sets (clusters) that satisfy a special property.
Factor Analysis (FA):
https://en.wikipedia.org/wiki/Factor_analysis
is a special technique to reduce the number of independent variables by searching those ones that seem to generate by linear regressions all existing:
"... Factor analysis aims to find independent latent variables...."
Hi Dr Haitham
just in short: Cluster analysis is concerned with grouping a set of objects (subjects, persons) in such a way that objects in the same group (cluster) are more similar to each other than to those in other groups.
Factor analysis is a method used for data reduction purposes. This method is concerned with grouping a set of variables and not objects. The basic idea is to represent a set of variables by a smaller number of variables, called factors.
Ragrads
Almaamari
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances between cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including parameters such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It is often necessary to modify data preprocessing and model parameters until the result achieves the desired properties.
The purpose of cluster analysis is to place objects into groups, or clusters, suggested by the data, not defined a priori, such that objects in a given cluster tend to be similar to each other in some sense, and objects in different clusters tend to be dissimilar. You can also use cluster analysis to summarize data rather than to find "natural" or "real" clusters; this use of clustering is sometimes called dissection. The SAS/STAT procedures for clustering are oriented toward disjoint or hierarchical clusters from coordinate data, distance data, or a correlation or covariance matrix.
https://en.wikipedia.org/wiki/Cluster_analysis
https://support.sas.com/rnd/app/stat/procedures/ClusterAnalysis.html
For business the huge volumes of data collected daily can be demanding and time consuming. Gathering, analyzing and reporting this type of information and discovering the most important data from the report can be supported through clustering it all. Here is a great article on this: https://www.dataentryoutsourced.com/blog/how-businesses-can-use-clustering-in-data-mining/
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