what is the difference between k-means and fuzzy c-means clustering?
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The difference is that in case of K-means, each element is assigned to only a single cluster, while in case if C-means, being a Fuzzy clustering technique, each element is assigned to all the available clusters with a different membership degree for each cluster.
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Data mining is more about digging data, discovering patterns and coming up with theories to get to inferences. But the methods of statistical analysis can be applied only on data that is cleansed. Statistics is more about confirmation and applying the various theories.
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An ideal way to figure out the right number of clusters would be to calculate the Within-Cluster-Sum-of-Squares (WCSS). WCSS is the sum of squares of the distances of each data point in all clusters to their respective centroids. The idea is to minimise the sum.
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Data warehouse refers to the process of compiling and organizing data into one common database, whereas data mining refers to the process of extracting useful data from the databases. The data mining process depends on the data compiled in the data warehousing phase to recognize meaningful patterns.
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Random Forest vs Gradient Boosting. Like random forests, gradient boosting is a set of decision trees. The two main differences are: ... Combining results: random forests combine results at the end of the process (by averaging or "majority rules") while gradient boosting combines results along the way.
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The main difference between data warehousing and data mining is that data warehousing is the process of compiling and organizing data into one common database, whereas data mining is the process of extracting meaningful data from that database. ... Whereas data mining aims to examine or explore the data using queries.
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k-Means Clustering is an unsupervised learning algorithm that is used for clustering whereas KNN is a supervised learning algorithm used for classification.
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Cluster analysis is a tool for classifying objects into groups and is not concerned with the geometric representation of the objects in a low-dimensional space. To explore the dimensionality of the space, one may use multidimensional scaling.
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Clustering models are different from predictive models in that the outcome of the process is not guided by a known result, that is, there is no target attribute. Predictive models predict values for a target attribute, and an error rate between the target and predicted values can be calculated to guide model building.
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Cluster analysis is somewhat exploratory. ... Cluster analysis tries to maximize in-group homogeneity and maximize between group heterogeneity. Multiple discriminant analysis is different. It starts with a discrete DV and tries to determine how much the levels of the IV's distinguish the members of the groups.
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KNN represents a supervised classification algorithm that will give new data points accordingly to the k number or the closest data points, while k-means clustering is an unsupervised clustering algorithm that gathers and groups data into k number of clusters.
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The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the data. This can be used to confirm business assumptions about what types of groups exist or to identify unknown groups in complex data sets.
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The 'K' in K-Means Clustering has nothing to do with the 'K' in KNN algorithm. k-Means Clustering is an unsupervised learning algorithm that is used for clustering whereas KNN is a supervised learning algorithm used for classification.
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As nouns the difference between group and cluster is that group is a number of things or persons being in some relation to one another while cluster is a group or bunch of several discrete items that are close to each other.
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The data used in cluster analysis can be interval, ordinal or categorical. However, having a mixture of different types of variable will make the analysis more complicated.
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The main difference between classical set theory and fuzzy set theory is that the latter admits to partial set membership. A classical or crisp set, then, is a fuzzy set that restricts its membership values to {0, 1}, the endpoints of the unit interval.
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Factor analysis is an exploratory statistical technique to investigate dimensions and the factor structure underlying a set of variables (items) while cluster analysis is an exploratory statistical technique to group observations (people, things, events) into clusters or groups so that the degree of association is ...