Cluster Analysis - Revision history

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 == See Also ==
-[[Analysis Effort Method]]<br />
+[[Analysis Process Model (APM)]]<br />
 [[Analysis Effort Method]]
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	The significance of cluster analysis is that it provides a powerful tool for making sense of complex data sets. By grouping together similar objects into categories, it helps to uncover structures and patterns in the data that would otherwise be difficult or impossible to detect. Cluster analysis can be used to identify trends and associations between different groups of items, as well as to identify groups of people with similar buying habits. Moreover, distance is an important measure used in cluster analysis which can help reveal the relationships between different sets of data points.		The significance of cluster analysis is that it provides a powerful tool for making sense of complex data sets. By grouping together similar objects into categories, it helps to uncover structures and patterns in the data that would otherwise be difficult or impossible to detect. Cluster analysis can be used to identify trends and associations between different groups of items, as well as to identify groups of people with similar buying habits. Moreover, distance is an important measure used in cluster analysis which can help reveal the relationships between different sets of data points.
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		+	In many applications, the notion of a cluster is not well defined. To better understand the difficulty of deciding what constitutes a cluster, consider The Figure below, which shows twenty points and three different ways of dividing them into clusters. The shapes of the markers indicate cluster membership. Figures (b) and (d) divide the data into two and six parts, respectively. However, the apparent division of each of the two larger clusters into three subclusters may simply be an artifact of the human visual system. Also, it may not be unreasonable to say that the points form four clusters, as shown in Figure (c). This figure illustrates that the definition of a cluster is imprecise and that the best definition depends on the nature of the data and the desired results.
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		+	[[File:Cluster Analysis.png\|700px\|Cluster Analysis.png]]
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		+	Cluster analysis is related to other techniques that are used to divide data objects into groups. For instance, clustering can be regarded as a form of classification in that it creates a labeling of objects with class (cluster) labels. However, it derives these labels only from the data.

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	== What are the different types of evaluation criteria? ==		== What are the different types of evaluation criteria? ==
	Different types of evaluation criteria can have a significant impact on the final decision. Some evaluation measures are based on intuition rather than data, which can lead to biased results. Other evaluation criteria are used to assess the quality of clustering algorithms, such as the Davies–Bouldin index and other measures that look at distances between elements in a cluster and the number of clusters. These different types of evaluation criteria need to be carefully assessed when making decisions so that an accurate prediction is made regarding cluster quality.		Different types of evaluation criteria can have a significant impact on the final decision. Some evaluation measures are based on intuition rather than data, which can lead to biased results. Other evaluation criteria are used to assess the quality of clustering algorithms, such as the Davies–Bouldin index and other measures that look at distances between elements in a cluster and the number of clusters. These different types of evaluation criteria need to be carefully assessed when making decisions so that an accurate prediction is made regarding cluster quality.
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		+	== See Also ==
		+	[[Analysis Effort Method]]<br />
		+	[[Analysis Effort Method]]
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