Sharif Digital Repository

In this research, a novel approach is developed to predict stocks return and risks. In this three stage method, through a comprehensive investigation all possible features which can be effective on stocks risk and return are identified. Then, in the next stage risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, on the basis of filter and function-based clustering; the important features in risk and return prediction are selected then risk and return re-predicted. The results show that the proposed hybrid model is a proper tool for effective feature selection and these features are good indicators for the prediction... 

Public platforms are one of the most important Cloud Manufacturing modes. Public platforms enable an environment for manufacturers and demanders to freely and directly connect with each other. Exploiting the high potentials of public platforms depends on final matching. This paper has developed a multi-phase matching mechanism for stable and optimal resource allocation in public platform. The proposed mechanism grades the demanders using an intuitionistic fuzzy VIKOR method using three measures of quality, time, and sustainability. Then, the mechanism clusters the demanders based on these three measures; and finally, allocates the clusters using the Deferred Acceptance (DA) algorithm to the... 

The set covering problem (SCP) has quite a large coefficient matrix in practical problems, but due to its low density, most of its entries are zero. Drawing on this observation, some methods can be developed to exploit special structures of the coefficient matrix of an SCP in such a way that they contain smaller dense subproblems. Against this backdrop, in this paper, three structures, namely 'partitioned', 'semi-block angular', and 'block angular', are proposed. To begin with, some heuristic methods are presented to exploit these structures of the coefficient matrices of SCPs; then, by optimally solving the smaller subproblems of these structures, their solutions are used to solve the whole... 

This paper considers the problem of secure data communication through the Global System for Mobile communications (GSM). The algebraic codebook method for data transmission through the Adaptive Multi Rate 12.2Kbps voice channel is investigated and its maximum achievable data rate is calculated. Based on the vocoder channel properties, the method's Bit Error Rate (BER) performance is improved by repetition coding and classification methods. Simulation results show that by simultaneous application of repetition coding and clustering methods, the decoder's performance improves about 6.5% compared to the case of no clustering for 1Kbps data communication in AMR 4.75 voice codec  

Clustering is one of the most useful methods of data mining, in which a set of real or abstract objects are categorized into clusters. The DBSCAN clustering method, one of the most famous density based clustering methods, categorizes points in dense areas into same clusters. In DBSCAN a point is said to be dense if the ε-radius circular area around it contains at least MinPts points. To find such dense areas, region queries are fired. Two points are defined as density connected if the distance between them is less than ε and at least one of them is dense. Finally, density connected parts of the data set extracted as clusters. The significant issue of such a method is that its parameters (ε... 

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... 

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... 

Most of clustered models in wireless sensor networks use a double-layered structure. In these structures a node is considered as cluster-head and has the responsibility of gathering information of environment. Static attribute is the main disadvantage of these clustering method. because as traffic rises in environment as time passes, cluster-head nodes energy and its close nodes, is consumed rapidly and so these nodes that has important role in data gathering, are break down. This issue is considered as age decrease in network life time and finally, network death. In this paper, a new three-layered dynamic model is introduced that its main goal is to increase network life time. The dynamic... 

Most positions of the human genome are typically invariant (99%) and only some positions (1%) are commonly variant which are associated with complex genetic diseases. Haplotype reconstruction is to divide aligned SNP fragments, which is the most frequent form of difference to address genetic diseases, into two classes, and thus inferring a pair of haplotypes from them. Minimum error correction (MEC) is an important model for this problem but only effective when the error rate of the fragments is low. MEC/GI as an extension to MEC employs the related genotype information besides the SNP fragments and so results in a more accurate inference. The haplotyping problem, due to its NP-hardness, may... 

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... 

The penetration of wind energy in power systems has been growing due to its interminable and mild environmental effects. The intrinsic attributes of this environmentally-friendly energy, i.e., the stochastic nature of wind farms generation, however, imposes various technical and financial challenges into power systems. So, developing an accurate wind farm modeling approach aimed at taking into account the wind generation intermittency can relieve many of these challenges. Therefore, this paper takes a step to an efficient wind farm modeling procedure employing an accurate as well as well-known Neural Network (NN)-based tool. The proposed approach is comprised of two main steps. The wind... 

Automotive Industry has an important place all around the world and sales forecasting process supports companies to meet their goals such as sales revenue increase, efficiency improvement, capacity planning and customer care. Traditional methods such as time series and econometrics have been applied by scientists during last decades. However, recently sales forecast problem by means of machine learning techniques are welcomed by data scientists because of increasing power of information technology in both hardware and software aspects. In this research, the hybridization of clustering method, Adaptive network Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) are developed... 

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  

Data stream clustering has attracted a huge attention in recent years. Many one-pass and evolving algorithms have been developed in this field but feature selection and its influence on clustering solution has not been addressed by these algorithms. In this paper we explain a feature-based clustering method for streaming data. Our method establishes a ranking between features based on their appropriateness in terms of clustering compactness and separateness. Then, it uses an automatic algorithm to identify unimportant features and remove them from feature set. These two steps take place continuously during lifetime of clustering task. © 2009 IEEE  

Clustering search results helps the user to overview returned results and to focus on the desired clusters. Most of search result clustering methods use title, URL and snippets returned by a search engine as the source of information for creating the clusters. In this paper we propose a new method for search results clustering (SRC) which uses social annotations as the main source of information about web pages. Social annotations are high-level descriptions for web pages and as the experiments show, clustering based on social annotations yields good clusters with informative labels. © 2008 Springer-Verlag  

Bayesian optimization algorithm (BOA) utilizes a Bayesian network to estimate the probability distribution of candidate solutions and creates the next generation by sampling the constructed Bayesian network. This paper proposes an improved real-coded BOA (IrBOA) for continuous global optimization. In order to create a set of Bayesian networks, the candidate solutions are partitioned by an adaptive clustering method. Each Bayesian network has its own structure and parameters, and the next generation is produced from this set of networks. The adaptive clustering method automatically determines the correct number of clusters so that the probabilistic building-block crossover (PBBC) is... 

One of the key constraints of using embedded speech recognition modules is the required computational power. To decrease this requirement, we propose an algorithm that clusters the speech signal before passing it to the recognition units. The algorithm is based on agglomerative clustering and produces a sequence of compressed frames, optimized for recognition. Our experimental results indicate that the proposed method presents a frame rate with average 40 frames per second on medium to large vocabulary isolated word recognition tasks without loss of recognition accuracy which result in up to 60% faster recognition in compare to usual 100 fps fixed frame rate sampling. This value is quite...