Different Clustering Techniques – Means for Improved Knowledge
... if the attribute has categorical or numerical value, if it should be used as input in model building or as an output attribute. There is also a possibility to declare certain attributes as unused or display-only, when they would not be used for building a model. Each column in MS Excel spreadsheet c ...
... if the attribute has categorical or numerical value, if it should be used as input in model building or as an output attribute. There is also a possibility to declare certain attributes as unused or display-only, when they would not be used for building a model. Each column in MS Excel spreadsheet c ...
a two-staged clustering algorithm for multiple scales
... meaning a high intra-class similarity and a low inter-class similarity. The quality of a clustering method is also measured by its ability to discover hidden patterns [1]. There are two kinds of clustering methods -- hierarchical and partitioning. This study used a k-means method (one of the popular ...
... meaning a high intra-class similarity and a low inter-class similarity. The quality of a clustering method is also measured by its ability to discover hidden patterns [1]. There are two kinds of clustering methods -- hierarchical and partitioning. This study used a k-means method (one of the popular ...
Selection of Initial Centroids for k
... Step 1: From n objects calculate a point whose attribute values are average of n-objects attribute values.so first initial centroid is average on n-objects. Step 2: select next initial centroids from n-objects in such a way that the Euclidean distance of that object is maximum from other selected in ...
... Step 1: From n objects calculate a point whose attribute values are average of n-objects attribute values.so first initial centroid is average on n-objects. Step 2: select next initial centroids from n-objects in such a way that the Euclidean distance of that object is maximum from other selected in ...
Document
... starts and pick up the best one as the result [24, 26]. Besides random starts, there are a number of initialization methods, most of which concentrate on how to intelligently choose the starting configurations (the K centers) in order to be as close to the global minima as possible [5, 25, 22, 17]. ...
... starts and pick up the best one as the result [24, 26]. Besides random starts, there are a number of initialization methods, most of which concentrate on how to intelligently choose the starting configurations (the K centers) in order to be as close to the global minima as possible [5, 25, 22, 17]. ...
Clustering high-dimensional data derived from Feature Selection
... to find a subset of features and effectiveness is related to the quality of the subset of features. It can be extended to use with multiple datasets [2]. Lei Yu, Huan Liu in” Efficient Feature Selection via Analysis of Relevance and Redundancy”- we show that feature relevance alone is insufficient f ...
... to find a subset of features and effectiveness is related to the quality of the subset of features. It can be extended to use with multiple datasets [2]. Lei Yu, Huan Liu in” Efficient Feature Selection via Analysis of Relevance and Redundancy”- we show that feature relevance alone is insufficient f ...
A clustering algorithm using the tabu search approach
... next iteration. The proposed tabu search approach with simulated annealing algorithm for cluster generation is as follows: Step 1: Generate an initial solution dnit using GLA algorithm. Set Ccurr = Cbest = Cinit- Set a counter Countj for each element in the solution, j = 1, 2,.. .T. T is the total n ...
... next iteration. The proposed tabu search approach with simulated annealing algorithm for cluster generation is as follows: Step 1: Generate an initial solution dnit using GLA algorithm. Set Ccurr = Cbest = Cinit- Set a counter Countj for each element in the solution, j = 1, 2,.. .T. T is the total n ...
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.