Introducing A Hybrid Data Mining Model to Evaluate Customer Loyalty
... behavior. In using k-means and k-medoids which are unable to identify the optimal number of clusters, clustering was performed in a range from 2 to 9 clusters and Davies-Bouldin index was calculated in each stage. Davies-Bouldin index (VDB) which has many applications uses similarity measure of two ...
... behavior. In using k-means and k-medoids which are unable to identify the optimal number of clusters, clustering was performed in a range from 2 to 9 clusters and Davies-Bouldin index was calculated in each stage. Davies-Bouldin index (VDB) which has many applications uses similarity measure of two ...
A Unified Framework for Model-based Clustering
... 2. A Unified Framework for Model-based Clustering In this section, we present a unifying bipartite graph view of probabilistic model-based clustering and demonstrate the benefits of having such a viewpoint. In Section 2.2, model-based partitional clustering is mathematically analyzed from a determin ...
... 2. A Unified Framework for Model-based Clustering In this section, we present a unifying bipartite graph view of probabilistic model-based clustering and demonstrate the benefits of having such a viewpoint. In Section 2.2, model-based partitional clustering is mathematically analyzed from a determin ...
Conceptual Grouping of Object Behaviour in
... quantitative patterns in data sets. When a large database is given, to discover the inherent patterns and regularities becomes a challenging task, especially when no domain knowledge is available or when domain knowledge is too weak. Because of the size of the database, it is almost impossible for a ...
... quantitative patterns in data sets. When a large database is given, to discover the inherent patterns and regularities becomes a challenging task, especially when no domain knowledge is available or when domain knowledge is too weak. Because of the size of the database, it is almost impossible for a ...
CLINCH: Clustering Incomplete High-Dimensional Data
... Given a set of unit, a cluster is the maximal of connected dense units. Two points are in the same cluster if the units they belong to are connected or there exist a set of dense units that are each other connected. Problem Statement: Given a set of data points D, desired number of intervals σ on ea ...
... Given a set of unit, a cluster is the maximal of connected dense units. Two points are in the same cluster if the units they belong to are connected or there exist a set of dense units that are each other connected. Problem Statement: Given a set of data points D, desired number of intervals σ on ea ...
ppt
... Build small clusters, then cluster small clusters into bigger clusters, and so on Divisive clustering algorithms ...
... Build small clusters, then cluster small clusters into bigger clusters, and so on Divisive clustering algorithms ...
Complex building`s energy system operation patterns analysis using
... existence to a specific event. A pattern is thus dependent on the characteristics of a system and may represent the underlying processes and structure of the system. Methods that can automatically identify interesting patterns from buildings’ data, help to get useful insights into the various parame ...
... existence to a specific event. A pattern is thus dependent on the characteristics of a system and may represent the underlying processes and structure of the system. Methods that can automatically identify interesting patterns from buildings’ data, help to get useful insights into the various parame ...
View/Open - ScholarWorks
... produced as a result [19]. Others have built upon these methods, by measuring the semantic similarity between text passages. Mihalcea et al evaluate the ...
... produced as a result [19]. Others have built upon these methods, by measuring the semantic similarity between text passages. Mihalcea et al evaluate the ...
Nearest-neighbor chain algorithm
In the theory of cluster analysis, the nearest-neighbor chain algorithm is a method that can be used to perform several types of agglomerative hierarchical clustering, using an amount of memory that is linear in the number of points to be clustered and an amount of time linear in the number of distinct distances between pairs of points. The main idea of the algorithm is to find pairs of clusters to merge by following paths in the nearest neighbor graph of the clusters until the paths terminate in pairs of mutual nearest neighbors. The algorithm was developed and implemented in 1982 by J. P. Benzécri and J. Juan, based on earlier methods that constructed hierarchical clusterings using mutual nearest neighbor pairs without taking advantage of nearest neighbor chains.