Soft clustering
Exponential family mixed membership models for soft?clustering of multivariate data
... model-based clustering methods have successfully tack- led many of the challenges presented by ...a soft clustering approach whereby observations are not restricted to single cluster membership, have ...
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Web Log Mining Based on Soft Clustering and Multi-Objective Genetic Algorithm
... called clustering. Clustering is unsupervised learning technique of data ...this clustering is k-means and k-median algorithm, but these algorithm are suffered some point of problem now used ...
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SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
... In this work, we propose the Sparse Compos- ite Document Vector(SCDV) representation learn- ing technique to address these challenges and cre- ate efficient, accurate and robust semantic repre- sentations of large texts ...
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Exploring Soft Clustering for German (Particle) Verbs across Frequency Ranges
... NMF clustering provides a membership score x ≥ 0 for each verb and each cluster, we assume that the higher the membership score of a verb for a certain cluster, the more likely the verb is to be part of ...the ...
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Stage Determination of Cancer in Mammogram Image using SOFT CLUSTERING and ANN
... Segmentation is the process of partitioning a digital image into multiple segments. By segmentation technique it is easy to change the representation of an image so it will be easier to analyze and it is easy to locate ...
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Unsubstantiated Machine Learning In For Delay Tolerance Mobile Networks
... The primary aim of maximizing (1) is to determine a clustering that reduces the latency within the network. To do so, the LPNs are clustered according to their channel strength, which means that each LPN ...
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Comparative Study of Different Clustering Algorithms
... hard clustering algorithms is the soft clustering ...In Soft clustering algorithms, the data point can belong to more than one cluster which depends on distance ...Fuzzy ...
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Clustering with Bregman Divergences
... general soft-clustering algorithm for all regu- lar Bregman ...Bregman clustering algorithms in terms of a trade-off between compression and loss in Bregman ...
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Soft context clustering for F0 modeling in HMM based speech synthesis
... a soft decision tree, to improve generalization performance compared to the conventional ‘ hard ’ decision tree method that is used to cluster context-dependent model parameters in statistical parametric speech ...
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Unsubstantiated Machine Learning In For Delay Tolerance Mobile Networks
... The primary aim of maximizing (1) is to determine a clustering that reduces the latency within the network. To do so, the LPNs are clustered according to their channel strength, which means that each LPN ...
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Soft document clustering using a novel graph covering approach
... hard clustering defines that every document belongs to only one cluster, whereas soft clustering allows documents to belong even to one or more clusters with a distinct ...soft ...
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Application of kohonen neural network and rough approximation for overlapping clusters optimization
... The approach is based on the rough set theory that employs a soft clustering which can detects overlapped data from the data set and makes clustering as precise as possible,[r] ...
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FCM : Fuzzy C-Means Clustering – A View in Different Aspects
... Bo Gao and Jun Wang [2] have used the technique called Fast Generalize C-Means (FGFCM) and Xie-Bie (XB) index. FGFCM incoperatates both information of spatial and gray image and produces the advance image. The only ...
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Improved Fuzzy C-Means Algorithm for Background Removal
... 1279 | P a g e vary from image to image. All the selected centroids update its location to the suitable position by using the membership function. These updated cluster centroids is used in FCM algorithm for ...
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International Journal of Computer Science and Mobile Applications
... nodes. Clustering is the most popular topology control method to reduce energy consumption and improve scalability of ...efficient clustering in WSN so that energy consumption can be ...developing ...
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Efficient hybrid reduction for binary based information system in soft set theory
... I would like to express the deepest appreciation to my supervisor, Prof. Dr Mustafa Mat Deris, who has been enthusiastically guiding and encouraging me in this PhD research. His untiring perseverance and relentless ...
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On Soft -Continuous Functions
... of soft sets was first introduced by Molodtsov [11] in 1999 as a general mathematical tool for dealing with uncertain ...the soft theory in several directions, such as smoothness of functions, game theory, ...
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Equality in soft erosion, soft dilation and soft close in multi scale environment
... as soft morphology. So, this soft morphology is having a few advantages, which the mathematical morphology operations don’t ...the Soft Morphology can be considered as extension to mathematical ...
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Unsupervised Translation Sense Clustering
... Monolingual features for K-Means clustering were computed from an English corpus of Web documents with 700 billion tokens of text. Bilin- gual features were computed from 0.78 (S→E) and 1.04 (J→E) billion tokens ...
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Soft Play Detection in Shooter Games Using Hit Matrix Analysis
... The time required to process a single game is on average less than one second, which is negligible compared to the length of a typical game. On a personal computer with AMD FX-8320 processor at 3.51 GHz and 16 GB of RAM ...
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