Loading...
Search for:
clustering-algorithms
0.01 seconds
Total 136 records
A new approach for sensitivity analysis in network flow problems
, Article International Journal of Industrial Engineering : Theory Applications and Practice ; Volume 27, Issue 1 , 2020 , Pages 72-87 ; Eshghi, K ; Salehipour, A ; Sharif University of Technology
University of Cincinnati
2020
Abstract
This paper proposes a new approach to study the sensitivity analysis in the network flow problems, in particular, the minimum spanning tree and shortest path problems. In a sensitivity analysis, one looks for the amount of changes in the edges’ weights, number of edges or number of vertices such that the optimal solution, i.e., the minimum spanning tree or shortest path does not change. We introduce a novel approach, and develop associated equations and mathematics. We discuss two illustrative examples to show the applicability of the proposed approach. © International Journal of Industrial Engineering
Application of Fuzzy C-means algorithm as a novel approach to predict solubility of hydrocarbons in carbon dioxide
, Article Petroleum Science and Technology ; Volume 36, Issue 4 , 2018 , Pages 308-312 ; 10916466 (ISSN) ; Garmsiri, H ; Zare, M ; Hemmati, N ; Sharif University of Technology
Taylor and Francis Inc
2018
Abstract
In the recent years, declination of oil reservoir causes the importance of researches on enhancement of oil recovery processes become more important. One of wide applicable approaches in enhancement of oil recovery is carbon dioxide injection which becomes interested because of relative low cost, good displacement and environmentally aspects. The injection of carbon dioxide to oil reservoir causes the lighter hydrocarbons of crude oil are extracted by CO2. This phenomena can be affected by various factors such the solubility of hydrocarbons in carbon dioxide so in the present investigation Fuzzy c-means (FCM) as a novel approach for estimation of solubility of alkanes in carbon dioxide in...
Active distance-based clustering using k-medoids
, Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 19 April 2016 through 22 April 2016 ; Volume 9651 , 2016 , Pages 253-264 ; 03029743 (ISSN) ; 9783319317526 (ISBN) ; Ghadiri, M ; Soleymani Baghshah, M ; Sharif University of Technology
Springer Verlag
2016
Abstract
k-medoids algorithm is a partitional, centroid-based clustering algorithm which uses pairwise distances of data points and tries to directly decompose the dataset with n points into a set of k disjoint clusters. However, k-medoids itself requires all distances between data points that are not so easy to get in many applications. In this paper, we introduce a new method which requires only a small proportion of the whole set of distances and makes an effort to estimate an upperbound for unknown distances using the inquired ones. This algorithm makes use of the triangle inequality to calculate an upper-bound estimation of the unknown distances. Our method is built upon a recursive approach to...
How to extend visibility polygons by mirrors to cover invisible segments
, Article 11th International Conference and Workshops on Algorithms and Computation, WALCOM 2017, 29 March 2017 through 31 March 2017 ; Volume 10167 LNCS , 2017 , Pages 42-53 ; 03029743 (ISSN); 9783319539249 (ISBN) ; Ghodsi, M ; Sharif University of Technology
Springer Verlag
2017
Abstract
Given a simple polygon P with n vertices, the visibility polygon (V P) of a point q (V P(q)), or a segment (formula present) (V P(pq)) inside P can be computed in linear time. We propose a linear time algorithm to extend V P of a viewer (point or segment), by converting some edges of P into mirrors, such that a given non-visible segment (formula present) can also be seen from the viewer. Various definitions for the visibility of a segment, such as weak, strong, or complete visibility are considered. Our algorithm finds every edge such that, when converted to a mirror, makes (formula present) visible to our viewer. We find out exactly which interval of (formula present) becomes visible, by...
Cluster-based sparse topical coding for topic mining and document clustering
, Article Advances in Data Analysis and Classification ; Volume 12, Issue 3 , 2018 , Pages 537-558 ; 18625347 (ISSN) ; Gholampour, I ; Tabandeh, M ; Sharif University of Technology
Springer Verlag
2018
Abstract
In this paper, we introduce a document clustering method based on Sparse Topical Coding, called Cluster-based Sparse Topical Coding. Topic modeling is capable of improving textual document clustering by describing documents via bag-of-words models and projecting them into a topic space. The latent semantic descriptions derived by the topic model can be utilized as features in a clustering process. In our proposed method, document clustering and topic modeling are integrated in a unified framework in order to achieve the highest performance. This framework includes Sparse Topical Coding, which is responsible for topic mining, and K-means that discovers the latent clusters in documents...
Attaining higher quality for density based algorithms
, Article 1st International Conference on Web Reasoning and Rule Systems, RR 2007, Innsbruck, 7 June 2007 through 8 June 2007 ; Volume 4524 LNCS , 2007 , Pages 329-338 ; 03029743 (ISSN); 354072981X (ISBN); 9783540729815 (ISBN) ; Abolhassani, H ; Haghir Chehreghani, M ; Sharif University of Technology
Springer Verlag
2007
Abstract
So far several methods have been proposed for clustering the web. On the other hand, many algorithms have been developed for clustering the relational data, but their usage for the Web is to be investigated. One main category of such algorithms is density based methods providing high quality results. In this paper first, a new density based algorithm is introduced and then it is compared with other algorithms of this category. The proposed algorithm has some interesting properties and capabilities such as hierarchical clustering and sampling, making it suitable for clustering the web data. © Springer-Verlag Berlin Heidelberg 2007
H-BayesClust: A new hierarchical clustering based on Bayesian networks
, Article 3rd International Conference on Advanced Data Mining and Applications, ADMA 2007, Harbin, 6 August 2007 through 8 August 2007 ; Volume 4632 LNAI , 2007 , Pages 616-624 ; 03029743 (ISSN); 9783540738701 (ISBN) ; Abolhassani, H ; Sharif University of Technology
Springer Verlag
2007
Abstract
Clustering is one of the most important approaches for mining and extracting knowledge from the web. In this paper a method for clustering the web data is presented which using a Bayesian network, finds appropriate representatives for each of the clusters. Having those representatives, we can create more accurate clusters. Also the contents of the web pages are converted into vectors which firstly, the number of dimensions is reduced, and secondly the orthogonality problem is solved. Experimental results show about the high quality of the resultant clusters. © Springer-Verlag Berlin Heidelberg 2007
Improved algorithms for distributed balanced clustering
, Article 3rd IFIP WG 1.8 International Conference on Topics in Theoretical Computer Science, TTCS 2020, 1 July 2020 through 2 July 2020 ; Volume 12281 LNCS , 2020 , Pages 72-84 ; Zarrabizadeh, H ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2020
Abstract
We study a weighted balanced version of the k-center problem, where each center has a fixed capacity, and each element has an arbitrary demand. The objective is to assign demands of the elements to the centers, so as the total demand assigned to each center does not exceed its capacity, while the maximum distance between centers and their assigned elements is minimized. We present a deterministic O(1)-approximation algorithm for this generalized version of the k-center problem in the distributed setting, where data is partitioned among a number of machines. Our algorithm substantially improves the approximation factor of the current best randomized algorithm available for the problem. We...
A content-based deep intrusion detection system
, Article International Journal of Information Security ; 2021 ; 16155262 (ISSN) ; Siavoshani, M. J ; Jahangir, A. H ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2021
Abstract
The growing number of Internet users and the prevalence of web applications make it necessary to deal with very complex software and applications in the network. This results in an increasing number of new vulnerabilities in the systems, and leading to an increase in cyber threats and, in particular, zero-day attacks. The cost of generating appropriate signatures for these attacks is a potential motive for using machine learning-based methodologies. Although there are many studies on using learning-based methods for attack detection, they generally use extracted features and overlook raw contents. This approach can lessen the performance of detection systems against content-based attacks...
A content-based deep intrusion detection system
, Article International Journal of Information Security ; Volume 21, Issue 3 , 2022 , Pages 547-562 ; 16155262 (ISSN) ; Siavoshani, M. J ; Jahangir, A. H ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2022
Abstract
The growing number of Internet users and the prevalence of web applications make it necessary to deal with very complex software and applications in the network. This results in an increasing number of new vulnerabilities in the systems, and leading to an increase in cyber threats and, in particular, zero-day attacks. The cost of generating appropriate signatures for these attacks is a potential motive for using machine learning-based methodologies. Although there are many studies on using learning-based methods for attack detection, they generally use extracted features and overlook raw contents. This approach can lessen the performance of detection systems against content-based attacks...
EACHP: Energy Aware Clustering Hierarchy Protocol for Large Scale Wireless Sensor Networks
, Article Wireless Personal Communications ; Volume 85, Issue 3 , December , 2015 , Pages 765-789 ; 09296212 (ISSN) ; Movaghar, A ; Rahmani, A. M ; Sharif University of Technology
Springer New York LLC
2015
Abstract
Wireless sensor networks (WSNs) comprise a large number of small sensor nodes scattered across limited geographical areas. The nodes in such networks carry sources of limited and mainly unchangeable energy. Therefore, it is necessary that these networks operate under energy efficient protocols and structures. Energy efficient clustering algorithms have been developed to reduce the networks energy consumption and extend its lifetime. This paper presents an innovative cluster-based communication protocol for WSNs. In order to reduce communication overhead, the authors propose an Energy Aware Clustering Hierarchy Protocol that creates a multi-level hierarchical structure to adequately route and...
CGC: centralized genetic-based clustering protocol for wireless sensor networks using onion approach
, Article Telecommunication Systems ; Volume 62, Issue 4 , 2016 , Pages 657-674 ; 10184864 (ISSN) ; Barati, H ; Movaghar, A ; Naghizadeh, A ; Sharif University of Technology
Springer New York LLC
Abstract
Wireless sensor networks consist of a large number of nodes which are distributed sporadically in a geographic area. The energy of all nodes on the network is limited. For this reason, providing a method of communication between nodes and network administrator to manage energy consumption is crucial. For this purpose, one of the proposed methods with high performance, is clustering methods. The big challenge in clustering methods is dividing network into several clusters that each cluster is managed by a cluster head (CH). In this paper, a centralized genetic-based clustering (CGC) protocol using onion approach is proposed. The CGC protocol selects the appropriate nodes as CHs according to...
A Task-Based Greedy Scheduling Algorithm for Minimizing Energy of MapReduce Jobs
, Article Journal of Grid Computing ; Volume 16, Issue 4 , 2018 , Pages 535-551 ; 15707873 (ISSN) ; Goudarzi, M ; Sharif University of Technology
Springer Netherlands
2018
Abstract
MapReduce and its open source implementation, Hadoop, have gained widespread adoption for parallel processing of big data jobs. Since the number of such big data jobs is also rapidly rising, reducing their energy consumption is increasingly more important to reduce environmental impact as well as operational costs. Prior work by Mashayekhy et al. (IEEE Trans. Parallel Distributed Syst. 26, 2720–2733, 2016), has tackled the problem of energy-aware scheduling of a single MapReduce job but we provide a far more efficient heuristic in this paper. We first model the problem as an Integer Linear Program to find the optimal solution using ILP solvers. Then we present a task-based greedy scheduling...
On the power allocation strategies in coordinated multi-cell networks using Stackelberg game
, Article Eurasip Journal on Wireless Communications and Networking ; Volume 2016, Issue 1 , 2016 ; 16871472 (ISSN) ; Oliaiee, A ; Behroozi, H ; Khalaj, B. H ; Sharif University of Technology
Springer International Publishing
Abstract
In this paper, we study the power allocation problem in multi-cell OFDMA networks, where given the tradeoff between user satisfaction and profit of the service provider, maximizing the revenue of the service provider is also taken into account. Consequently, two Stackelberg games are proposed for allocating proper powers to central and cell-edge users. In our algorithm, assuming the fact that users agree to pay more for better QoS level, the service provider imposes optimum prices for unit-power transmitted to users as they request different levels of QoS. In addition, in order to improve system performance at cell-edge locations, users are divided into two groups based on their distance to...
Learning a metric when clustering data points in the presence of constraints
, Article Advances in Data Analysis and Classification ; Volume 14, Issue 1 , 2020 , Pages 29-56 ; Bashiri, M. A ; Beigy, H ; Sharif University of Technology
Springer
2020
Abstract
Learning an appropriate distance measure under supervision of side information has become a topic of significant interest within machine learning community. In this paper, we address the problem of metric learning for constrained clustering by considering three important issues: (1) considering importance degree for constraints, (2) preserving the topological structure of data, and (3) preserving some natural distribution properties in the data. This work provides a unified way to handle different issues in constrained clustering by learning an appropriate distance measure. It has modeled the first issue by injecting the importance degree of constraints directly into an objective function....
A three-dimensional statistical volume element for histology informed micromechanical modeling of brain white matter
, Article Annals of Biomedical Engineering ; Volume 48, Issue 4 , 2020 , Pages 1337-1353 ; Farahmand, F ; Ahmadian, M. T ; Sharif University of Technology
Springer
2020
Abstract
This study presents a novel statistical volume element (SVE) for micromechanical modeling of the white matter structures, with histology-informed randomized distribution of axonal tracts within the extracellular matrix. The model was constructed based on the probability distribution functions obtained from the results of diffusion tensor imaging as well as the histological observations of scanning electron micrograph, at two structures of white matter susceptible to traumatic brain injury, i.e. corpus callosum and corona radiata. A simplistic representative volume element (RVE) with symmetrical arrangement of fully alligned axonal fibers was also created as a reference for comparison. A...
Robust fuzzy rough set based dimensionality reduction for big multimedia data hashing and unsupervised generative learning
, Article Multimedia Tools and Applications ; Volume 80, Issue 12 , 2021 , Pages 17745-17772 ; 13807501 (ISSN) ; Majidi, B ; Adabi, S ; Patra, J. C ; Movaghar, A ; Sharif University of Technology
Springer
2021
Abstract
The amount of high dimensional data produced by visual sensors in the smart environments and by autonomous vehicles is increasing exponentially. In order to search and model this data for real-time applications, the dimensionality of the data should be reduced. In this paper, a novel dimensionality reduction algorithm based on fuzzy rough set theory, called Centralized Binary Mapping (CBM), is proposed. The fuzzy CBM kernel is used for extracting the central elements and the memory cells from the blocks of high dimensional data. The proposed applications of CBM in this paper include hashing and generative modelling of multimedia big data. The robustness of the proposed CBM based hashing...
Optimized age dependent clustering algorithm for prognosis: A case study on gas turbines
, Article Scientia Iranica ; Volume 28, Issue 3 B , 2021 , Pages 1245-1258 ; 10263098 (ISSN) ; Durali, M ; Abbasian Najafabadi, T ; Saadat Foumani, M ; Sharif University of Technology
Sharif University of Technology
2021
Abstract
This paper proposes an Age-Dependent Clustering (ADC) structure to be used for prognostics. To achieve this aim, a step-by-step methodology is introduced, that includes clustering, reproduction, mapping, and finally estimation of Remaining Useful Life (RUL). In the mapping step, a neural fitting tool is used. To clarify the age-based clustering concept, the main elements of the ADC model is discussed. A Genetic algorithm (GA) is used to find the elements of the optimal model. Lastly, the fuzzy technique is applied to modify the clustering. By investigating a case study on the health monitoring of some turbofan engines, the efficacy of the proposed method is demonstrated. The results showed...
Complexity of computing the anti-ramsey numbers for paths
, Article 45th International Symposium on Mathematical Foundations of Computer Science, MFCS 2020, 25 August 2020 through 26 August 2020 ; Volume 170 , 2020 ; Popa, A ; Roghani, M ; Shahkarami, G ; Soltani, R ; Vahidi, H ; Sharif University of Technology
Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
2020
Abstract
The anti-Ramsey numbers are a fundamental notion in graph theory, introduced in 1978, by Erdös, Simonovits and Sós. For given graphs G and H the anti-Ramsey number ar(G, H) is defined to be the maximum number k such that there exists an assignment of k colors to the edges of G in which every copy of H in G has at least two edges with the same color. Usually, combinatorists study extremal values of anti-Ramsey numbers for various classes of graphs. There are works on the computational complexity of the problem when H is a star. Along this line of research, we study the complexity of computing the anti-Ramsey number ar(G, Pk), where Pk is a path of length k. First, we observe that when k is...
Communities detection for advertising by futuristic greedy method with clustering approach
, Article Big Data ; Volume 9, Issue 1 , 2021 , Pages 22-40 ; 21676461 (ISSN) ; Izadi, M ; Sharif University of Technology
Mary Ann Liebert Inc
2021
Abstract
Community detection in social networks is one of the advertising methods in electronic marketing. One of the approaches to find communities in large social networks is to use greedy methods, because these methods perform very fast. Greedy methods are generally designed based on local decisions; thus, inappropriate local decisions may result in an improper global solution. The use of a greedy improved index with a futuristic approach can, to some extent, prevent inappropriate local choices. Our proposed method determines the influential nodes in the social network based on the followers and following and new futuristic greedy index. It classifies the nodes based on the influential nodes by...