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A novel clustering algorithm based on circlusters to find arbitrary shaped clusters
, Article 2008 International Conference on Computer and Electrical Engineering, ICCEE 2008, Phuket, 20 December 2008 through 22 December 2008 ; January , 2008 , Pages 619-624 ; 9780769535043 (ISBN) ; Habibi, J ; Abolhassani, H ; Shirali Shahreza, S ; Sharif University of Technology
2008
Abstract
Clustering is the problem of partitioning a (large) set of data using unsupervised techniques. Today, there exist many clustering techniques. The most important characteristic of a clustering technique is the shape of the cluster it can find. In this paper, we propose a method that is capable to find arbitrary shaped clusters and uses simple geometric constructs, Circlusters. Circlusters are different radius sectored circles. Circlusters can be used to create many hybrid approaches in mixture with density based or partitioning based methods. We also proposed two new clustering methods that are capable to find complex clusters in O(n), where n is the size of the data set. Both of the methods...
Circluster: storing cluster shapes for clustering
, Article 2008 4th International IEEE Conference Intelligent Systems, IS 2008, Varna, 6 September 2008 through 8 September 2008 ; Volume 3 , 2008 , Pages 1114-1119 ; 9781424417391 (ISBN) ; Hassas Yeganeh, S ; Abolhassani, H ; Habibi, J ; Sharif University of Technology
2008
Abstract
One of the important problems in knowledge discovery from data is clustering. Clustering is the problem of partitioning a set of data using unsupervised techniques. An important characteristic of a clustering technique is the shape of the cluster it can find. Clustering methods which are capable to find simple cluster shapes are usually fast but inaccurate for complex data sets. Ones capable to find complex cluster shapes are usually not fast but accurate. In this paper, we propose a simple clustering technique named circlusters. Circlusters are circles partitioned into different radius sectors. Circlusters can be used to create hybrid approaches with density based or partitioning based...
A search for an intermediate-mass black hole in the core of the globular cluster NGC 6266
, Article Astrophysical Journal ; Volume 745, Issue 2 , 2012 ; 0004637X (ISSN) ; Harrison, T. E ; Baumgardt, H ; Khalaj, P ; Sharif University of Technology
Abstract
It has long been thought that intermediate-mass black holes (IMBHs) might be located in the cores of globular clusters. However, studies attempting to confirm this possibility have been inconclusive. To refine the search for these objects, Baumgardt et al. completed a series of N-body simulations to determine the observational properties that a host globular cluster should possess. Keys to revealing the presence of an IMBH were found to be the shape of the cluster's core proper motion dispersion profile and its surface density profile. Among the possible host clusters identified by Baumgardt et al., NGC6266 was found to be the most suitable object to search. Hubble Space Telescope Wide Field...
A cluster analysis of the KM field
, Article Management Decision ; Volume 47, Issue 5 , 2009 , Pages 792-805 ; 00251747 (ISSN) ; Bontis, N ; Sharif University of Technology
2009
Abstract
Purpose: The main purpose of this study is to review the knowledge management literature from a content-related perspective using cluster analysis. Design/methodology/approach: A critical analysis of previous review articles in KM provided a conceptual framework with nine dimensions. A survey was then administered to 120 KM authors asking them to review which dimensions they considered in their own research. Findings: Three clusters of KM research were identified as follows: the socialization school, the collaboration school, and the codification school. Research limitations/implications: The study does not consider the dimension of strategic versus operational KM issues nor does it consider...
GDCluster: A general decentralized clustering algorithm
, Article IEEE Transactions on Knowledge and Data Engineering ; Volume 27, Issue 7 , 2015 , Pages 1892-1905 ; 10414347 (ISSN) ; Habibi, J ; Khalafbeigi, T ; Voulgaris, S ; Van Steen, M ; Sharif University of Technology
IEEE Computer Society
2015
Abstract
In many popular applications like peer-to-peer systems, large amounts of data are distributed among multiple sources. Analysis of this data and identifying clusters is challenging due to processing, storage, and transmission costs. In this paper, we propose GDCluster, a general fully decentralized clustering method, which is capable of clustering dynamic and distributed data sets. Nodes continuously cooperate through decentralized gossip-based communication to maintain summarized views of the data set. We customize GDCluster for execution of the partition-based and density-based clustering methods on the summarized views, and also offer enhancements to the basic algorithm. Coping with...
Novel approaches in cancer management with circulating tumor cell clusters
, Article Journal of Science: Advanced Materials and Devices ; Volume 4, Issue 1 , 2019 , Pages 1-18 ; 24682284 (ISSN) ; Kashaninejad, N ; Moshksayan, K ; Saidi, M. S ; Firoozabadi, B ; Nguyen, N. T ; Sharif University of Technology
Elsevier B.V
2019
Abstract
Tumor metastasis is responsible for the vast majority of cancer-associated morbidities and mortalities. Recent studies have disclosed the higher metastatic potential of circulating tumor cell (CTC) clusters than single CTCs. Despite long-term study on metastasis, the characterizations of its most potent cellular drivers, i.e., CTC clusters have only recently been investigated. The analysis of CTC clusters offers new intuitions into the mechanism of tumor metastasis and can lead to the development of cancer diagnosis and prognosis, drug screening, detection of gene mutations, and anti-metastatic therapeutics. In recent years, considerable attention has been dedicated to the development of...
GoSCAN: Decentralized scalable data clustering
, Article Computing ; Volume 95, Issue 9 , 2013 , Pages 759-784 ; 0010485X (ISSN) ; Habibi, J ; Voulgaris, S ; Van Steen, M ; Sharif University of Technology
2013
Abstract
Identifying clusters is an important aspect of analyzing large datasets. Clustering algorithms classically require access to the complete dataset. However, as huge amounts of data are increasingly originating from multiple, dispersed sources in distributed systems, alternative solutions are required. Furthermore, data and network dynamicity in a distributed setting demand adaptable clustering solutions that offer accurate clustering models at a reasonable pace. In this paper, we propose GoScan, a fully decentralized density-based clustering algorithm which is capable of clustering dynamic and distributed datasets without requiring central control or message flooding. We identify two major...
Local graph clustering with network lasso
, Article IEEE Signal Processing Letters ; Volume 28 , 2021 , Pages 106-110 ; 10709908 (ISSN) ; Sarcheshmehpour, Y ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2021
Abstract
We study the statistical and computational properties of a network Lasso method for local graph clustering. The clusters delivered by nLasso can be characterized elegantly via network flows between cluster boundaries and seed nodes. While spectral clustering methods are guided by a minimization of the graph Laplacian quadratic form, nLasso minimizes the total variation of cluster indicator signals. As demonstrated theoretically and numerically, nLasso methods can handle very sparse clusters (chain-like) which are difficult for spectral clustering. We also verify that a primal-dual method for non-smooth optimization allows to approximate nLasso solutions with optimal worst-case convergence...
DSCLU: A new data stream CLUstring algorithm for multi density environments
, Article Proceedings - 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012 ; 2012 , Pages 83-88 ; 9780769547619 (ISBN) ; Esfandani, G ; Sharif University of Technology
2012
Abstract
Recently, data stream has become popular in many contexts of data mining. Due to the high amount of incoming data, traditional clustering algorithms are not suitable for this family of problems. Many data stream clustering algorithms proposed in recent years considered the scalability of data, but most of them did not attend the following issues: (1) The quality of clustering can be dramatically low over the time. (2) Some of the algorithms cannot handle arbitrary shapes of data stream and consequently the results are limited to specific regions. (3) Most of the algorithms have not been evaluated in multi-density environments. Identifying appropriate clusters for data stream by handling the...
GDCLU: A new grid-density based clustring algorithm
, Article Proceedings - 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012, 8 August 2012 through 10 August 2012 ; August , 2012 , Pages 102-107 ; 9780769547619 (ISBN) ; Sayyadi, M ; Namadchian, A ; Sharif University of Technology
2012
Abstract
This paper addresses the density based clustering problem in data mining where clusters are established based on density of regions. The most well-known algorithm proposed in this area is DBSCAN [1] which employs two parameters influencing the shape of resulted clusters. Therefore, one of the major weaknesses of this algorithm is lack of ability to handle clusters in multi-density environments. In this paper, a new density based grid clustering algorithm, GDCLU, is proposed which uses a new definition for dense regions. It determines dense grids based on densities of their neighbors. This new definition enables GDCLU to handle different shaped clusters in multi-density environments. Also...
Probabilistic heuristics for hierarchical web data clustering
, Article Computational Intelligence ; Volume 28, Issue 2 , 2012 , Pages 209-233 ; 08247935 (ISSN) ; Haghir Chehreghani, M ; Abolhassani, H ; Sharif University of Technology
Abstract
Clustering Web data is one important technique for extracting knowledge from the Web. In this paper, a novel method is presented to facilitate the clustering. The method determines the appropriate number of clusters and provides suitable representatives for each cluster by inference from a Bayesian network. Furthermore, by means of the Bayesian network, the contents of the Web pages are converted into vectors of lower dimensions. The method is also extended for hierarchical clustering, and a useful heuristic is developed to select a good hierarchy. The experimental results show that the clusters produced benefit from high quality
Extracting activated regions of fMRI data using unsupervised learning
, Article Proceedings of the International Joint Conference on Neural Networks, 14 June 2009 through 19 June 2009, Atlanta, GA ; 2009 , Pages 641-645 ; 9781424435531 (ISBN) ; Taalimi, A ; Fatemizadeh, E ; International Neural Network Society; IEEE Computational Intelligence Society ; Sharif University of Technology
2009
Abstract
Clustering approaches are going to efficiently define the activated regions of the brain in fMRI studies. However, choosing appropriate clustering algorithms and defining optimal number of clusters are still key problems of these methods. In this paper, we apply an improved version of Growing Neural Gas algorithm, which automatically operates on the optimal number of clusters. The decision criterion for creating new clusters at the heart of this online clustering is taken from MB cluster validity index. Comparison with other so-called clustering methods for fMRI data analysis shows that the proposed algorithm outperforms them in both artificial and real datasets. ©2009 IEEE
A clustering fuzzification algorithm based on ALM
, Article Fuzzy Sets and Systems ; Volume 389 , 2020 , Pages 93-113 ; Malekzadeh, A ; Heydari, G ; Bagheri Shouraki, S ; Sharif University of Technology
Elsevier B.V
2020
Abstract
In this paper, we propose a fuzzification method for clusters produced from a clustering process, based on Active Learning Method (ALM). ALM is a soft computing methodology which is based on a hypothesis claiming that human brain interprets information in pattern-like images. The proposed fuzzification method is applicable to all non-fuzzy clustering algorithms as a post process. The most outstanding advantage of this method is the ability to determine the membership degrees of each data to all clusters based on the density and shape of the clusters. It is worth mentioning that for existing fuzzy clustering algorithms such as FCM the membership degree is usually determined as a function of...
A fuzzy clustering algorithm for finding arbitrary shaped clusters
, Article 6th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2008, Doha, 31 March 2008 through 4 April 2008 ; 2008 , Pages 559-566 ; 9781424419685 (ISBN) ; Bagheri Shouraki, S ; Sharif University of Technology
2008
Abstract
Until now, many algorithms have been introduced for finding arbitrary shaped clusters, but none of these algorithms is able to identify all sorts of cluster shapes and structures that are encountered in practice. Furthermore, the time complexity of the existing algorithms is usually high and applying them on large dataseis is time-consuming. In this paper, a novel fast clustering algorithm is proposed. This algorithm distinguishes clusters of different shapes using a twostage clustering approach. In the first stage, the data points are grouped into a relatively large number of fuzzy ellipsoidal sub-clusters. Then, connections between sub-clusters are established according to the Bhatiacharya...
A novel semi-supervised clustering algorithm for finding clusters of arbitrary shapes
, Article 13th International Computer Society of Iran Computer Conference on Advances in Computer Science and Engineering, CSICC 2008, Kish Island, 9 March 2008 through 11 March 2008 ; Volume 6 CCIS , 2008 , Pages 876-879 ; 18650929 (ISSN); 3540899847 (ISBN); 9783540899846 (ISBN) ; Bagheri Shouraki, S ; Sharif University of Technology
2008
Abstract
Recently, several algorithms have been introduced for enhancing clustering quality by using supervision in the form of constraints. These algorithms typically utilize the pair wise constraints to either modify the clustering objective function or to learn the clustering distance measure. Very few of these algorithms show the ability of discovering clusters of different shapes along with satisfying the provided constraints. In this paper, a novel semi-supervised clustering algorithm is introduced that uses the side information and finds clusters of arbitrary shapes. This algorithm uses a two-stage clustering approach satisfying the pair wise constraints. In the first stage, the data points...
Robust Clustering Using Outlier-Sparsity Regularization
, M.Sc. Thesis Sharif University of Technology ; Daneshgar, Amir (Supervisor)
Abstract
Although clustering algorithms such as k-means and probabilistic clustering are quite popular and widely used nowadays, their performance are too sensitive to the presence of outliers where Even few outliers can compromise the ability of these algorithms to extract hidden data substructures. In this thesis, after going through the basics of some optimization methods such as BCD, EM, and MM, in Section 2 and a review of relevant clustering methods in Section 3, we explore the results of [Forero, et al., Robust clustering using outlier-sparsity regularization. IEEE Trans. Signal Process. (60), 2012] in Sections 4 and 5 where the outliers are handled by introducing a regularization term in the...
Using minimum matching for clustering with balancing constraints
, Article 2009 Second ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM 2009, Sanya, 8 August 2009 through 9 August 2009 ; Volume 1 , 2009 , Pages 225-228 ; 9781424442461 (ISBN) ; Abolhassani, H ; Shirali Shahreza, M. H ; Yangzhou University; Guangdong University of Business Studies; Wuhan Institute of Technology; IEEE SMC TC on Education Technology and Training; IEEE Technology Management Council ; Sharif University of Technology
2009
Abstract
Clustering is a major task in data mining which is used in many applications. However, general clustering is inappropriate for many applications where some constraints should be applied. One category of these constraints is the cluster size constraint. In this paper, we propose a new algorithm for solving the clustering with balancing constraints by using the minimum matching. We compare our algorithm with the method proposed by Banerjee and Ghosh that uses stable matching and show that our algorithm converge to the final solution in fewer iterations. ©2009 IEEE
Thermal entanglement of spins in mean-field clusters
, Article Physical Review A - Atomic, Molecular, and Optical Physics ; Volume 73, Issue 6 , 2006 ; 10502947 (ISSN) ; Karimipour, V ; Sharif University of Technology
2006
Abstract
We determine thermal entanglement in mean-field clusters of N spin one-half particles interacting via the anisotropic Heisenberg interaction, with and without external magnetic field. For the xxx cluster in the absence of magnetic field we prove that only the N=2 ferromagnetic cluster shows entanglement. An external magnetic field B can only entangle xxx antiferromagnetic clusters in certain regions of the B-T plane. On the other hand, the xxz clusters of size N>2 are entangled only when the interaction is ferromagnetic. Detailed dependence of the entanglement on various parameters is investigated in each case. © 2006 The American Physical Society
Exploiting structural information of data in active learning
, Article Artificial Intelligence and Soft Computing: Lecture Notes in Computer Science ; Volume 8468 LNAI, Issue PART 2 , 2014 , Pages 796-808 ; Beigy, Hamid ; Haghiri, Siavash ; Sharif University of Technology
Abstract
In recent years, the active learning algorithms have focused on combining correlation criterion and uncertainty criterion for evaluating instances. Although these criteria might be useful, applying these measures on whole input space globally may lead to inefficient selected instances for active learning. The proposed method takes advantage of clustering to partition input space to subspaces. Then it exploits both labeled and unlabeled data locally for selection of instances by using a graph-based active learning. We define a novel utility score for selecting clusters by combining uncertainty criterion, local entropy of clusters and the factor of contribution of each cluster in queries....
Electronic and Structural Properties of Neutral, Anionic, and Cationic RhxCu4-x (x = 0-4) Small Clusters: A DFT Study
, Article Journal of Cluster Science ; Volume 24, Issue 1 , 2013 , Pages 273-287 ; 10407278 (ISSN) ; Gobal, F ; Nahali, N ; Nahali, M ; Sharif University of Technology
2013
Abstract
In this study, electronic structure, stability, and tendency to exchange electron of neutral, anionic, and cationic RhxCu4-x (x = 0-4) small clusters were investigated by density functional theory calculations. For neutral small clusters, it was found that the most stable structures of Rh4, Rh3Cu and Rh2Cu2 have distorted tetrahedral shape while the most stable structures of RhCu3 and Cu4 have quasi-planer shape. Adding charges to the clusters, caused shapes of the most stable structures undergo variations. Stabilities of the neutral, anionic, and cationic clusters decrease linearly with increasing the copper content. In addition, calculated chemical harnesses indicated that the small...