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Unsupervised change detection via hierarchical support vector clustering

Frank de Morsier, Devis Tuia, Volker Gass, Jean-Philippe Thiran, Maurice Borgeaud
2012 7th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)  
We build a series of nested hierarchical support vector clustering descriptions, select the appropriate one using a cluster validity measure and finally merge the clusters into two classes, corresponding  ...  Experiments on two multispectral datasets confirm the power and appropriateness of the proposed system.  ...  For outliers, memberships are assigned to the closest cluster using Euclidean distance. Clustering validity measure (CVM).  ... 
doi:10.1109/pprs.2012.6398309 fatcat:uocwt6m5mbbojbigf6arl7fs7a

Penalty Parameter Selection for Hierarchical Data Stream Clustering

Amol Bhagat, Nilesh Kshirsagar, Priti Khodke, Kiran Dongre, Sadique Ali
2016 Procedia Computer Science  
It also compares the performance analysis of the different algorithms under hierarchical clustering techniques for data streams.  ...  Identifying the number of clusters required for the precise clustering of data streams is an open research area. This paper gives the overview of the hierarchical data stream clustering algorithms.  ...  is put up into a suitable form for further processing; clustering is applied to the data considering the matching algorithm, presentation of result and the choice of parameters; and validation of the  ... 
doi:10.1016/j.procs.2016.03.005 fatcat:fgyfgl7rsnh5vbi7ggmbkka47e

Hierarchical Clustering Algorithms In Data Mining

Z. Abdullah, A. R. Hamdan
2015 Zenodo  
Therefore, in this paper we do survey and review four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON and BIRCH.  ...  Clustering algorithms in one of the area in data mining and it can be classified into partition, hierarchical, density based and grid based.  ...  Therefore, noises and outliers must be removed at early stages of clustering to ensure that the valid data points shouldn't fall into the wrong clusters.  ... 
doi:10.5281/zenodo.1109341 fatcat:lyzjj7ef2jggfkdfv2n7q42qea

Term Frequency Based Cosine Similarity Measure for Clustering Categorical Data using Hierarchical Algorithm

S. Anitha Elavarasi, J. Akilandeswari
2015 Research Journal of Applied Sciences Engineering and Technology  
Results are evaluated for vote real life data set using TFCSM based hierarchical clustering and standard hierarchical clustering algorithm using single link, complete link and average link method.  ...  In this study performance of cosine based hierarchical clustering algorithm for categorical data is evaluated.  ...  Data set: Real life dataset, such as Congressional Vote is obtained from UCI machine learning repository (Lichman, 2013) Measure for cluster validation: The cluster validation is the process of evaluating  ... 
doi:10.19026/rjaset.11.2043 fatcat:2mjrabf7dbhotpsyekfk5z4erq

PREREQIR: Recovering Pre-Requirements via Cluster Analysis

Jane Huffman Hayes, Giuliano Antoniol, Yann-Gaël Guéhéneuc
2008 2008 15th Working Conference on Reverse Engineering  
We automatically label the common and outlier PRI (82% correctly labeled), and obtain 74% accuracy for the similarity threshold of 0.36 (78% for a threshold of 0.5).  ...  We present a method using partition around medoids and agglomerative clustering for obtaining, structuring, analyzing, and labeling textual PRI from a group of diverse stakeholders.  ...  We briefly discuss outliers and the labels of the merged PRI. Understanding Outliers.  ... 
doi:10.1109/wcre.2008.36 dblp:conf/wcre/HayesAG08 fatcat:tftnbrqa3bdo7f33krzwszuijm

Unsupervised clustering of materials properties using hierarchical techniques

Arafa S. Sobh, Sameh A. Salem, Rania Darwish, Mohammed Hussein, Omar Karam
2015 International Journal of Collaborative Enterprise  
It adopts the hierarchal clustering for mining engineering materials properties.  ...  In addition, a study of different similarity measures is carried out to choose the best fit Unsupervised clustering of materials properties 75 similarity measure to engineering material datasets.  ...  Hierarchal clustering techniques overcome the problem involved with k-means and also can be considered as robust and effective techniques for materials decision support systems (Chauhan and Vaish, 2013  ... 
doi:10.1504/ijcent.2015.073182 fatcat:7uosantezfab3ivoexccd6ei7y

SpaRef: a clustering algorithm for multispectral images

Thanh N. Tran, Ron Wehrens, Lutgarde M.C. Buydens
2003 Analytica Chimica Acta  
In this work, spatial refinement clustering (SpaRef), a new clustering algorithm for multispectral images is presented.  ...  Many segmentation methods for multispectral images are based on a per-pixel classification, which uses only spectral information and ignores spatial information.  ...  Acknowledgements We thank Gertjan Geerling for sharing the data and stimulating discussions.  ... 
doi:10.1016/s0003-2670(03)00720-7 fatcat:44c5mhzcvbegdicv4hcaouykbe

Performance Evaluation of Weighted Time-Parameterized Edit

2016 International Journal of Science and Research (IJSR)  
Hierarchical RFID Trajectory Clustering uses similarity measure called; Time-parameterized Edit Distance (TED) is improvised weightage for the different parameters.  ...  Also, this model can deal with variants in both time and space dimensions and the clustering algorithm are much less sensitive to noise and outliers than existing methods.  ...  Using the hierarchical clustering algorithm, this path will be treated as a different path than p1, instead of an outlier.  ... 
doi:10.21275/v5i6.nov164802 fatcat:jxpgxkzdwjcqlaafbb3outhmsq

An Efficient Agglomerative Clustering Algorithm for Web Navigation Pattern Identification

A. Anitha
2016 Circuits and Systems  
Rough set based clustering with validity measure 0.54 Generation of dense clusters is essential for finding interesting patterns needed for further mining and analysis.  ...  This paper also deals with the problem of assessing the quality of user session clusters and cluster validity is measured by using statistical test, which measures the distances of clusters distributions  ...  Fast merging with time complexity O(N 2 ) 2. Exact Similarity Measure & No Approximation • Outliers are eliminated automatically during agglomeration.  ... 
doi:10.4236/cs.2016.79205 fatcat:ifjmc7rc65hbrkuga7lzkykxgq

An Analysis of Outlier Detection through clustering method

T. Chandrakala, S. Nirmala Sugirtha Rajini
2020 International Journal of Advanced engineering Management and Science  
The approaches used in this research are 1) Centroid based approach based on K-Means and Hierarchical Clustering algorithm and 2) through Clustering based approach.  ...  This approach may help in detecting outlier by grouping all similar elements in the same group. For grouping, the elements clustering method paves a way for it.  ...  Hierarchical Clustering A hierarchical clustering system operates by grouping info items to some tree of clusters.  ... 
doi:10.22161/ijaems.612.13 fatcat:w7c55r4y2fdvrndhx7lelwrtaa

An Accurate Grid -based PAM Clustering Method for Large Dataset

Faisal BinAlAbid, M.A. Mottalib
2012 International Journal of Computer Applications  
Therefore, the proposed approach has higher accuracy and provides natural clustering method which scales well for large dataset.  ...  Our proposed Grid Multi-dimensional K-medoids (GMK) algorithm uses the concept of cluster validity index and it is shown from the experimental results that the new proposed method has higher accuracy than  ...  If Cluster (size) of a cell is less than or equal to 5% of maximum number of data points in a cell the cell is not considered as outlier grid and the cell is not used for merging in order to produce the  ... 
doi:10.5120/5821-7808 fatcat:7v3p7o4nlnc5zn5dhydmlaxsqm

A Density Based Dynamic Data Clustering Algorithm based on Incremental Dataset

2012 Journal of Computer Science  
However, if we measure the performance with a cluster validation metric, then it will give another kind of result.  ...  So to evaluate the performance of the algorithms, we used Generalized Dunn Index (GDI), Davies-Bouldin index (DB) as the cluster validation metric and as well as time taken for clustering.  ...  ACKNOWLEDGEMENT We thank to Management, Principal and Secretary of Info Institute of Engineering for providing facility to implement this study.  ... 
doi:10.3844/jcssp.2012.656.664 fatcat:wjmef7w5r5h2znafokowvvcd2u

A Review of Clustering Techniques with FBPN

Sasirekha J, Dr. Savithri V
2014 International Journal of Computer & Organization Trends  
Image processing algorithms are used to extract the information and patterns derived by process.  ...  This study predicts the best supervised learning method of clustering techniques in fuzzy back propagation network(FBPN).  ...  The above minimum and maximum measures represent two extremes in measuring the distance between clusters. They tend to be overly sensitive to outliers or noisy data.  ... 
doi:10.14445/22492593/ijcot-v8p302 fatcat:7fr56hp5qjdefl6g2rofjukm54

Clustering Methods for Electricity Consumers: An Empirical Study in Hvaler-Norway [article]

The-Hien Dang-Ha, Roland Olsson, Hao Wang
2017 arXiv   pre-print
This enables the application of data mining techniques for traditional problems in power system.  ...  We also proposed a good way to extract consumption patterns for each consumer. The grouping results were assessed using four common internal validity indexes.  ...  Figure Validity Indexes for Assessing Clustering Before diving into clustering methods, first we need to discuss how we measure the quality of clustering results.  ... 
arXiv:1703.02502v1 fatcat:jb5b6xqxnfh57kamod6suudvwu

Clustering of online learning resources via minimum spanning tree

Qingyuan Wu, Changchen Zhan, Fu Lee Wang, Siyang Wang, Zeping Tang
2016 AAOU Journal  
Resources with quite low densities are identified as outliers and therefore removed.  ...  Confronting such a large amount of learning data, it is important to develop effective clustering approaches for user group modeling and intelligent tutoring. The paper aims to discuss these issues.  ...  The classical K-means clustering, averagelink hierarchical clustering, complete-link hierarchical clustering, and DBSCAN method are implemented in this study for comparison.  ... 
doi:10.1108/aaouj-09-2016-0036 fatcat:jgptj3d2mfg25ota6sv6luwzru
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