The Nearest Sub-class Classifier: a Compromise between the
... data set contains an unknown amount of noise in the features and class labels, so that the exact position of the training objects in feature space is uncertain. Second, the training data may be an undersampling of the true data distribution. Unfortunately this is often the case, so that the model as ...
... data set contains an unknown amount of noise in the features and class labels, so that the exact position of the training objects in feature space is uncertain. Second, the training data may be an undersampling of the true data distribution. Unfortunately this is often the case, so that the model as ...
A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior
... The other direct solution to the supervised clustering problem, due to Finley and Joachims (2005), is based on the SVMs for Interdependent and Structured Outputs technique (Tsochantaridis et al., 2004). In this model, a particular clustering method, correlation clustering, is held fixed, and weights ...
... The other direct solution to the supervised clustering problem, due to Finley and Joachims (2005), is based on the SVMs for Interdependent and Structured Outputs technique (Tsochantaridis et al., 2004). In this model, a particular clustering method, correlation clustering, is held fixed, and weights ...
APRIORI ALGORITHM AND FILTERED ASSOCIATOR IN
... association relationships among a huge database has been known to be useful in selective marketing, decision analysis, and business management. A popular area of applications is the market basket analysis, which studies the buying behaviors of customers by searching for sets of items that are freque ...
... association relationships among a huge database has been known to be useful in selective marketing, decision analysis, and business management. A popular area of applications is the market basket analysis, which studies the buying behaviors of customers by searching for sets of items that are freque ...
Life-and-Death Problem Solver in Go
... dead or unsettled. Alive means that the surrounded group does not need to be defended because it cannot be killed, i.e. it is unnecessary (indeed pointless) to play a stone in the surrounded area to secure (or to attack) the surrounded group. Unsettled is a situation where, if the owner of the surro ...
... dead or unsettled. Alive means that the surrounded group does not need to be defended because it cannot be killed, i.e. it is unnecessary (indeed pointless) to play a stone in the surrounded area to secure (or to attack) the surrounded group. Unsettled is a situation where, if the owner of the surro ...
PIVE: Per-Iteration Visualization Environment for
... approximates a nonnegative matrix X as the product of two low-rank nonnegative matrices W and H, which can be interpreted as cluster representatives and membership coefficients, respectively, in the clustering context. One can compute yi as the largest element index in the i-th column of H. NMF iter ...
... approximates a nonnegative matrix X as the product of two low-rank nonnegative matrices W and H, which can be interpreted as cluster representatives and membership coefficients, respectively, in the clustering context. One can compute yi as the largest element index in the i-th column of H. NMF iter ...
A Hybrid Clustering Algorithm for Outlier Detection in Data
... deals with finding a structure in a collection of unlabelled data (Aggarwal. et.al., 2004). Hierarchical clustering algorithms recursively nested clusters either in agglomerative method by starting with each data point in its own cluster and merging with similar pair of clusters successively to form ...
... deals with finding a structure in a collection of unlabelled data (Aggarwal. et.al., 2004). Hierarchical clustering algorithms recursively nested clusters either in agglomerative method by starting with each data point in its own cluster and merging with similar pair of clusters successively to form ...
Clustering Text Data Streams - Department of Computer Science
... Messaging (IM) and Internet Relay Chat (IRC) text message streams are classified[1] . In such text data stream applications, text data comes as a continuous stream and this presents many challenges to traditional static text clustering. For example, the whole text data cannot be fit into memory at o ...
... Messaging (IM) and Internet Relay Chat (IRC) text message streams are classified[1] . In such text data stream applications, text data comes as a continuous stream and this presents many challenges to traditional static text clustering. For example, the whole text data cannot be fit into memory at o ...
Data Mining for extraction of fuzzy IF
... a pre-establish number of clusters to group; generating 47 rules (presented in Tables VII and VIII), although they were more than in any other technique, the error shows it was the closest solution for the Mackey-Glass time series problem. We show in Fig. 5 and Fig. 7 the input variables cluster for ...
... a pre-establish number of clusters to group; generating 47 rules (presented in Tables VII and VIII), although they were more than in any other technique, the error shows it was the closest solution for the Mackey-Glass time series problem. We show in Fig. 5 and Fig. 7 the input variables cluster for ...
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.