Supervised Clustering
Enteromorpha Prolifera Detection with MODIS Image Using Semi-supervised Clustering
... labelled data and a large amount of unlabelled data for the automation of green tide monitoring. With few labels, we generated pairwise constraints for semi-supervised clustering and then adopted metric ...
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DATA CONFIDENTIALITY ON SEMI SUPERVISED CLUSTERING
... semi- supervised clustering, which uses a small amount of supervised data in the form of class labels or pairwise constraints on some examples to aid unsupervised ...Semi-supervised ...
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A Review article on Semi Supervised Clustering Framework for High Dimensional Data
... Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data ...several clustering algorithms ...
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Semi Supervised Clustering Ensemble Approaches Over Multiple Datasets
... semi-supervised clustering is ...In clustering at testing stage to unlabeled data if star shape picture is coming then it can be group in blossom name cluster; like this comparative kind shape to be ...
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Model selection for semi-supervised clustering
... semi-supervised clustering, which can be divided into four major categories: (i) use all data: in this na¨ıve approach, all data objects, including those in- volved in labels or constraints, are used when ...
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Semi-supervised clustering of fractionated electrograms for electroanatomical atrial mapping
... semi-supervised clustering facilitates the automatic detection of fractionation classes with accuracy comparable to other similar results reported in the literature, avoiding the manual labeling of AF ...
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Semi supervised Clustering of Medical Text
... 1999). Clustering is useful for applications where the goal is to find structure in a collection of documents, and can be applied in a wide range of tasks, such as finding groups among patients with breast cancer, ...
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Semi Supervised Clustering for Short Answer Scoring
... In our SAS scenario, we assume that there is a one-to- many rather than a one-to-one relation between scores and clusters. I.e. one score (out of the maximum of 4 differ- ent scores for the ASAP dataset) can contain ...
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Semi supervised Clustering for Short Text via Deep Representation Learning
... Existing semi-supervised clustering methods fall into two categories: constraint-based and representation-based. In constraint-based meth- ods (Davidson and Basu, 2007), some labeled information is used to ...
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Partitioning The Documents Based On Semi-supervised Clustering Method.
... semi-supervised clustering algorithm is to maximize the throughput ...the clustering but also time saving.Semi-supervised algorithmis based on the two metrics: i) minimize total processing ...
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Ensembled Semi Supervised Clustering Approach for High Dimensional Data
... semi-supervised clustering ensemble approach. Both are successfully used for clustering gene expression ...semi-supervised clustering ensemble approach, RSSCE first adopts the random ...
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A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior
... the supervised clustering problem, the generic prob- lem encountered in tasks such as reference matching, coreference resolution, identity uncertainty and record ...Our clustering model is based on ...
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Enhanced Semi-Supervised Clustering
... Document Clustering via Active Learning with Pairwise Constraints” They present [3] active learning framework for document ...semi-supervised clustering with the current set of constraints to produce ...
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A New Homogeneity Inter Clusters Measure in Semi Supervised Clustering
... In our work, we presented an overview of semi-supervised clustering methods. Specifically, we introduced an overview of the various methods in this trend. A major limitation has characterized these ...
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Semi-Supervised Clustering for High Dimensional Data Clustering
... as supervised clustering, unsupervised clustering and semi ...of clustering. Clustering algorithms are based on active learning, with ensemble clustering-means algorithm, data ...
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Using Bilingual Comparable Corpora and Semi supervised Clustering for Topic Tracking
... k-means clustering with the EM al- gorithm, where labeled data provides prior infor- mation about the conditional distribution of hid- den category labels(Basu, ...
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Fuzzy Supervised Clustering Algorithm with the Particle Swarm Optimization
... fuzzy clustering algorithm is developed to obtain better quality of fuzzy clustering ...fuzzy clustering algorithm gives more accurate clustering results than the FCM algorithm for a real data ...
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Semi-supervised consensus clustering for gene expression data analysis
... consensus clustering method, designed an algorithm, and compared it with another semi-supervised clustering algorithm, a consensus clustering algorithm and a simple clustering algorithm ...
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A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering
... The proposed algorithm SSIFCM is an intuitionistic approach towards the process of semi-supervised clustering technique. The proposed algorithm is compared with FCM, SSFCM and some supervised ...
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Clustering Based Stratified Seed Sampling for Semi Supervised Relation Classification
... each clustering algorithm on the development data set and report their F-scores in Table ...golden clustering (GOLD), in which all instances are grouped in terms of their annotated ground relation major ...
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