A Multinomial Clustering Model for Fast Simulation of Computer
... knowledge, in the computer architecture literature, only the K-means algorithm has been applied [3] to this problem. This is in contrast to the amount of previous work done on clustering text, where several data mining techniques have been examined [11, 12, 13]. It has been shown that when K-means i ...
... knowledge, in the computer architecture literature, only the K-means algorithm has been applied [3] to this problem. This is in contrast to the amount of previous work done on clustering text, where several data mining techniques have been examined [11, 12, 13]. It has been shown that when K-means i ...
Online Pattern recognition in subsequence time series clustering
... apply for clustering. In the next step, subsequences will be clustered according to the similarity matrix and finally, results of clustering will be appeared. Around 10 years ago, some of the researchers have done time series clustering, but, it was shown that the results are meaningless [33]. For e ...
... apply for clustering. In the next step, subsequences will be clustered according to the similarity matrix and finally, results of clustering will be appeared. Around 10 years ago, some of the researchers have done time series clustering, but, it was shown that the results are meaningless [33]. For e ...
Clustering Genes using Gene Expression and Text Literature Data
... clustering to gene expression data, and then used text from abstracts to resolve hierarchical cluster boundaries to identify clusters that are functionally more coherent (Raychaudhuri et al., 2003). While previous approaches made use of both data types, they tended to ignore the correlation structur ...
... clustering to gene expression data, and then used text from abstracts to resolve hierarchical cluster boundaries to identify clusters that are functionally more coherent (Raychaudhuri et al., 2003). While previous approaches made use of both data types, they tended to ignore the correlation structur ...
High Dimensional Object Analysis Using Rough
... Abstract: High dimensional feature selection and data assignment is an important feature for high dimensional object analysis. In this work, we propose a new hybrid approach of combining attribute reduction of the Rough-set theory with Grey relation clustering. Designing clustering becomes increasin ...
... Abstract: High dimensional feature selection and data assignment is an important feature for high dimensional object analysis. In this work, we propose a new hybrid approach of combining attribute reduction of the Rough-set theory with Grey relation clustering. Designing clustering becomes increasin ...
cougar^2: an open source machine learning and data mining
... method. This presents significant safety risks, in that if there is a configuration error, it will be discovered when building the model. Thus, the only way to know if the model can be built is to finish building it. This causes problems when the algorithm takes a long time. C. Experiments The abili ...
... method. This presents significant safety risks, in that if there is a configuration error, it will be discovered when building the model. Thus, the only way to know if the model can be built is to finish building it. This causes problems when the algorithm takes a long time. C. Experiments The abili ...
MISSING VALUE IMPUTATION USING FUZZY POSSIBILISTIC C
... Quality data mining results can be obtained only with high quality input data. So missing data in data sets should be estimated to increase data quality. Here comes the importance of efficient methods for imputation of missing values. If the values are Missing At Random (MAR), it can be estimated us ...
... Quality data mining results can be obtained only with high quality input data. So missing data in data sets should be estimated to increase data quality. Here comes the importance of efficient methods for imputation of missing values. If the values are Missing At Random (MAR), it can be estimated us ...
Visually Mining Through Cluster Hierarchies
... one of the Lp -norms for a database of feature vectors). In contrast to DBSCAN, OPTICS does not assign cluster memberships but computes an ordering in which the objects are processed and additionally generates the information which would be used by an extended DBSCAN algorithm to assign cluster memb ...
... one of the Lp -norms for a database of feature vectors). In contrast to DBSCAN, OPTICS does not assign cluster memberships but computes an ordering in which the objects are processed and additionally generates the information which would be used by an extended DBSCAN algorithm to assign cluster memb ...
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.