Entropy-based Subspace Clustering for Mining Numerical Data
... units in di erent subspaces. The CLIQUE algorithm can be divided into the following three steps: (1) Find dense units and identify subspaces containing clusters. (2) Identify clusters in the selected subspace. (3) Generate minimal description for the clusters in disjunctive normal form. Although it ...
... units in di erent subspaces. The CLIQUE algorithm can be divided into the following three steps: (1) Find dense units and identify subspaces containing clusters. (2) Identify clusters in the selected subspace. (3) Generate minimal description for the clusters in disjunctive normal form. Although it ...
Clustering (1)
... This top-down strategy does the reverse of agglomerative hierarchical clustering by starting with all objects in one cluster. It subdivides the cluster into smaller and smaller ...
... This top-down strategy does the reverse of agglomerative hierarchical clustering by starting with all objects in one cluster. It subdivides the cluster into smaller and smaller ...
Distance based fast hierarchical clustering method for large datasets
... of the data points is unequal (i.e., number of the data points of abnormal/attack type is very less compared to normal data points). These low proportional data points (abnormal/attack data points) look like outliers in the feature space. These abnormal data points are likely to get merged with the ...
... of the data points is unequal (i.e., number of the data points of abnormal/attack type is very less compared to normal data points). These low proportional data points (abnormal/attack data points) look like outliers in the feature space. These abnormal data points are likely to get merged with the ...
A Two-Step Method for Clustering Mixed Categroical and Numeric
... present whole categorical attributes in cluster center, Yin et al. [17] and Ahmad et al. [18] list all items to represent cluster center. The similarity of categorical attributes is calculated based on the proportion of items’ appearance. He et al. [19] calculates the distance between clusters and o ...
... present whole categorical attributes in cluster center, Yin et al. [17] and Ahmad et al. [18] list all items to represent cluster center. The similarity of categorical attributes is calculated based on the proportion of items’ appearance. He et al. [19] calculates the distance between clusters and o ...
ST-DBSCAN: An algorithm for clustering spatial–temporal data
... This paper presents a new density-based clustering algorithm ST-DBSCAN, which is based on the algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) [5]. In DBSCAN, the density associated with a point is obtained by counting the number of points in a region of specified radius ...
... This paper presents a new density-based clustering algorithm ST-DBSCAN, which is based on the algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) [5]. In DBSCAN, the density associated with a point is obtained by counting the number of points in a region of specified radius ...
Customer Segmentation for Decision Support
... customers based on their behavior,we can better target their actions, such as launching tailored products, target one-to-one marketing and to meet the customer expectations. However, the problem often is that the data regarding customer behavior is available in several different sources and analyzin ...
... customers based on their behavior,we can better target their actions, such as launching tailored products, target one-to-one marketing and to meet the customer expectations. However, the problem often is that the data regarding customer behavior is available in several different sources and analyzin ...
Longitudinal Cluster Analysis with Dietary Data Over Time
... Cluster analysis is a useful tool for identifying data patterns that may not be apparent from unviariate or bivariate analyses. As such, it can be valuable in the data mining arsenal. Meanwhile, using macros greatly increases the ease of implementing programming solutions when multiple data sets or ...
... Cluster analysis is a useful tool for identifying data patterns that may not be apparent from unviariate or bivariate analyses. As such, it can be valuable in the data mining arsenal. Meanwhile, using macros greatly increases the ease of implementing programming solutions when multiple data sets or ...
DenGraph-HO: A Density-based Hierarchical Graph Clustering
... purpose is to bundle of single objects into groups in such a way that objects of the same group are more similar to each other than to objects of other groups. K-means (MacQueen 1967) is a commonly used and well studied clustering algorithm for spatial data. The algorithm strictly groups data all po ...
... purpose is to bundle of single objects into groups in such a way that objects of the same group are more similar to each other than to objects of other groups. K-means (MacQueen 1967) is a commonly used and well studied clustering algorithm for spatial data. The algorithm strictly groups data all po ...
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.