Hassan Abolhassani

Sharif University of Technology, Teheran, Tehrān, Iran

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Publications (62)10.64 Total impact

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    ABSTRACT: Social tagging is a process in which many users add metadata to a shared content. Through the past few years, the popularity of social tagging has grown on the Web. In this paper we investigated the use of social tags for Web page classification: adding new Web pages to an existing Web directory. A Web directory is a general human-edited directory of Web pages. It classifies a collection of pages into a wide range of hierarchical categories. The problem with manual construction and maintenance of Web directories is the significant need of time and effort by human experts. Our proposed method is based on applying different automatic approaches of using social tags for extending Web directories with new URLs.
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    ABSTRACT: Application update at run-time remains a challenging issue in software engineering. There are many techniques with different evaluation metrics, resulting in different behaviours in the application being updated. In this paper, we provide an extensive review of research work on dynamic software updating. A framework for the evaluation of dynamic updating features is developed, and the articles are categorized and discussed based on the provided framework. Areas of online software maintenance requiring further research are also identified and highlighted. This information is deemed to not only assist practitioners in selecting appropriate dynamic updating techniques for their systems, but also to facilitate the ongoing and continuous research in the field of dynamic software updating. Copyright © 2012 John Wiley & Sons, Ltd.
    Journal of Software: Evolution and Process 05/2013; 25(5). DOI:10.1002/smr.1556 · 1.27 Impact Factor
  • Zahra Narimani, Hamid Beigy, Hassan Abolhassani
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    ABSTRACT: Multiple sequence alignment (MSA) is one of the basic and important problems in molecular biology. MSA can be used for different purposes including finding the conserved motifs and structurally important regions in protein sequences and determine evolutionary distance between sequences. Aligning several sequences cannot be done in polynomial time and therefore heuristic methods such as genetic algorithms can be used to find approximate solutions of MSA problems. Several algorithms based on genetic algorithms have been developed for this problem in recent years. Most of these algorithms use very complicated, problem specific and time consuming mutation operators. In this paper, we propose a new algorithm that uses a new way of population initialization and simple mutation and recombination operators. The strength of the proposed GA is using simple mutation operators and also a special recombination operator that does not have problems of similar recombination operators in other GAs. The experimental results show that the proposed algorithm is capable of finding good MSAs in contrast to existing methods, while it uses simple operators with low computational complexity.
    International Journal of Computational Intelligence and Applications 02/2013; 11(04). DOI:10.1142/S146902681250023X
  • Zohre Karimi, Hassan Abolhassani, Hamid Beigy
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    ABSTRACT: Incremental learning has been used extensively for data stream classification. Most attention on the data stream classification paid on non-evolutionary methods. In this paper, we introduce new incremental learning algorithms based on harmony search. We first propose a new classification algorithm for the classification of batch data called harmony-based classifier and then give its incremental version for classification of data streams called incremental harmony-based classifier. Finally, we improve it to reduce its computational overhead in absence of drifts and increase its robustness in presence of noise. This improved version is called improved incremental harmony-based classifier. The proposed methods are evaluated on some real world and synthetic data sets. Experimental results show that the proposed batch classifier outperforms some batch classifiers and also the proposed incremental methods can effectively address the issues usually encountered in the data stream environments. Improved incremental harmony-based classifier has significantly better speed and accuracy on capturing concept drifts than the non-incremental harmony based method and its accuracy is comparable to non-evolutionary algorithms. The experimental results also show the robustness of improved incremental harmony-based classifier.
    Journal of Intelligent Information Systems 10/2012; 39(2). DOI:10.1007/s10844-012-0199-2 · 0.63 Impact Factor
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    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.
    Computational Intelligence 05/2012; 28(2). DOI:10.1111/j.1467-8640.2012.00414.x · 0.87 Impact Factor
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    ABSTRACT: A huge amount of web services are deployed on the Web, nowadays. These services can be used to fulfill online requests. Requests are getting more and more complicated over time. So, there exists a lot of frequent request that cannot be fulfilled using just one web service. For using web services, composing individual services to create the added-value composite web service to fulfill the user request is necessary in most cases. Web services can be composed manually but it is a too tedious and time consuming task. The ability of automatic web service composition to create a new composite web service is one of the key enabling features for the future for the semantic web. There are some successful methods for automatic web service composition, but the lack of standard, open, and lightweight test environment makes the comparison and evaluation of these composition methods impossible. In this paper we propose an architecture for a light weight and scalable testbed to execute, test and evaluate automatic web service composition algorithms. The architecture provides mandatory components for implementing and evaluation of automatic web service composition algorithms. Also, this architecture provides some extension mechanisms to extend its default functionalities. We have also given reference implementations for web service matchmaking and composition. Also, some scenarios for testing and evaluating the testbed are given. We have found that the performance of the composition method will dramatically decrease as the number of web services increases.
    Computers & Electrical Engineering 09/2010; 36(5):805-817. DOI:10.1016/j.compeleceng.2008.04.007 · 0.99 Impact Factor
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    ABSTRACT: Nowadays, e-learning platforms are widely used by universities and other research-based and educational institutions. Despite lots of advantages these educational environments provide for organizations, yet there are many unresolved problems which cause instructors and training managers with some difficulties to get proper information about the students' learning behavior. On one hand, lack of tools to measure, assess, and evaluate the performance of learners in educational activities has led the educators to fail to guarantee the success of learning process. On the other hand, strict structure of learning materials prevents students to acquire knowledge based on their learning style. Consequently, developing tools monitor and analyze the learner's interaction with e-learning environment is necessary. Business intelligence (BI) and On Line Analytical Processing (OLAP) technologies can be used in order to monitor and analyze the learner's behavior and performance in e-learning environments. They can also be used to evaluate the structure of the course content and its effectiveness in the learning process. This article investigates the use of business intelligence and OLAP tools in e-learning environments and presents a case study of how to apply these technologies in the database of an e-learning system. The study shows that students spend little time with course courseware and prefer to use collaborative activities, such as virtual classroom and forums instead of just viewing the learning material.
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    ABSTRACT: Fetching Lexico-Syntactic patterns from text rely on pairs of words (positive instances) that represent the target relation, and finding their simultaneous occurrence in text corpus. Due to existence of WordNet thesaurus (which contains the semantic relationship between words), collecting positive instances is easy. In non-english languages, it's hard to collect large number of positive instances in various contexts. We investigated some new ideas for collecting them in Persian language and finally run the best one and collected approximately 6,000 positive instances.
  • Gholamreza Esfandani, Hassan Abolhassani
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    ABSTRACT: A good approach in data mining is density based clustering in which the clusters are constructed based on the density of shape regions. The prominent algorithm proposed in density based clustering family is DBSCAN [1] that uses two global density parameters, namely minimum number of points for a dense region and epsilon indicating the neighborhood distance. Among others, one of the weaknesses of this algorithm is its un-suitability for multi-density data sets where different regions have various densities so the same epsilon does not work. In this paper, a new density based clustering algorithm, MSDBSCAN, is proposed. MSDBSCAN uses a new definition for core point and dense region. The MSDBSCAN can find clusters in multi-variant density data sets. Also this algorithm benefits scale independency. The results obtained on data sets show that the MSDBSCAN is very effective in multi-variant environment.
    Advanced Data Mining and Applications - 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part I; 01/2010
  • S. Kharazmi, A.F. Nejad, H. Abolhassani
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    ABSTRACT: Progressive use of Web based information retrieval systems such as general purpose search engines and dynamic nature of the Web make it necessary to continually maintain Web based information retrieval systems. Crawlers facilitate this process by following hyperlinks in Web pages to automatically download new and updated Web pages. Freshness (recency) is one of the important maintaining factors of Web search engine crawlers that takes weeks to months. Many large Web crawlers start from seed pages, fetch every links from them, and continually repeat this process without any policies that help them to better crawling and improving performance of those. We believe that data mining techniques can help us to improve the freshness parameter by extracting knowledge from crawling data. In this paper we propose a Web crawler that uses extracted knowledge by data mining techniques as policies for crawling. For this purpose we include a component to collect additional crawling information. This crawler starts by non-preferential crawling. After a few crawling, it trained by using mining techniques on crawling data and then uses policies for preferential crawling to improve freshness time. Our research represented that crawling with determined polices has better freshness than generic general purpose Web crawlers.
    Internet Technology and Secured Transactions, 2009. ICITST 2009. International Conference for; 12/2009
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    ABSTRACT: World Wide Web is a huge information space, making it a valuable resource for decision making. However, it should be effectively managed for such a purpose. One important management technique is clustering the web data. In this paper, we propose some developments in clustering methods to achieve higher qualities. At first we study a new density based method adapted for hierarchical clustering of web documents. Then utilizing the hyperlink structure of web, we propose a new method that incorporates density concepts with web graph. These algorithms have the preference of low complexity and as experimental results reveal, the resultant clusters have high quality.
    Decision Support Systems 11/2009; DOI:10.1016/j.dss.2009.04.002 · 2.04 Impact Factor
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    Mehrdad Mahdavi, Hassan Abolhassani
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    ABSTRACT: Fast and high quality document clustering is a crucial task in organizing information, search engine results, enhancing web crawling, and information retrieval or filtering. Recent studies have shown that the most commonly used partition-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm can generate a local optimal solution. In this paper we propose a novel Harmony K-means Algorithm (HKA) that deals with document clustering based on Harmony Search (HS) optimization method. It is proved by means of finite Markov chain theory that the HKA converges to the global optimum. To demonstrate the effectiveness and speed of HKA, we have applied HKA algorithms on some standard datasets. We also compare the HKA with other meta-heuristic and model-based document clustering approaches. Experimental results reveal that the HKA algorithm converges to the best known optimum faster than other methods and the quality of clusters are comparable.
    Data Mining and Knowledge Discovery 06/2009; 18(3):370-391. DOI:10.1007/s10618-008-0123-0 · 1.74 Impact Factor
  • Mohsen Jafari Asbagh, Mohsen Sayyadi, Hassan Abolhassani
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    ABSTRACT: Although dimension reduction techniques for text documents can be used for preprocessing of blogs, these techniques will be more effective if they deal with the nature of the blogs properly. In this paper we propose a shallow summarization method for blogs as a preprocessing step for blog mining which benefits from specific characteristics of the blogs including blog themes, time interval between posts, and body-title composition of the posts. We use our method for summarizing a collection of Persian blogs from PersianBlog hosting and investigate its influence on blog clustering.
    05/2009: pages 157-167;
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    ABSTRACT: The growth of database application usage requires database management systems (DBMS) that are accessible, reliable, and dependable. One approach to handle these requirements is replication mechanism. Replication mechanism can be divided into various categories. Some related works consider two categories for replication mechanisms: heterogeneous and homogenous however majority of them classify them in three groups: physical, trigger-based and log-based schema. Log-based replication mechanisms are the most widely used category among DBMS vendors. Adapting such approach for heterogeneous systems is a complex task, because of lack of log understanding in the other end. Semantic technologies provide a suitable framework to address heterogeneity problems in large scale and dynamic resources. In this paper we introduce a new approach to tackle replication problem in a heterogeneous environment by utilizing ontologies.
    Advances in Databases, Knowledge, and Data Applications, 2009. DBKDA '09. First International Conference on; 04/2009
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    ABSTRACT: Social tagging is a process in which many users add metadata to a shared content. Through the past few years, the popularity of social tagging has grown on the Web. In this paper we investigated the use of social tags for Web page classification: adding new Web pages to an existing Web directory. A Web directory is a general human-edited directory of Web pages. It classifies a collection of pages into a wide range of hierarchical categories. The problem with manual construction and maintenance of Web directories is the significant need of time and effort by human experts. Our proposed method is based on applying different automatic approaches of using social tags for extending Web directories with new URLs.
    Proceedings IEEE CSE'09, 12th IEEE International Conference on Computational Science and Engineering, August 29-31, 2009, Vancouver, BC, Canada; 01/2009
  • Proceedings of the Third International Conference on Weblogs and Social Media, ICWSM 2009, San Jose, California, USA, May 17-20, 2009; 01/2009
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    ABSTRACT: Relation extraction is a challenging task in natural language processing. Syntactic features are recently shown to be quite effective for relation extraction. In this paper, we generalize the state of the art syntactic convolution tree kernel introduced by Collins and Duffy. The proposed generalized kernel is more flexible and customizable, and can be conveniently utilized for systematic generation of more effective application specific syntactic sub-kernels. Using the generalized kernel, we will also propose a number of novel syntactic sub-kernels for relation extraction. These kernels show a remarkable performance improvement over the original Collins and Duffy kernel in the extraction of ACE-2005 relation types.
    Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, May 31 - June 5, 2009, Boulder, Colorado, USA, Student Research Workshop and Doctoral Consortium; 01/2009
  • Mohsen Jafari Asbagh, Hassan Abolhassani
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    ABSTRACT: 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.
    8th IEEE/ACIS International Conference on Computer and Information Science, IEEE/ACIS ICIS 2009, June 1-3, 2009, Shanghai, China; 01/2009
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    Majid Yazdani, Milad Eftekhar, Hassan Abolhassani
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    ABSTRACT: One important problem proposed recently in the field of web mining is website classification problem. The complexity together with the necessity to have accurate and fast algorithms yield to many attempts in this field, but there is a long way to solve these problems efficiently, yet. The importance of the problem encouraged us to work on a new approach as a solution. We use the content of web pages together with the link structure between them to improve the accuracy of results. In this work we use Naïve-bayes models for each predefined webpage class and an extended version of Hidden Markov Model is used as website class models. A few sample websites are adopted as seeds to calculate models' parameters. For classifying the websites we represent them with tree structures and we modify the Viterbi algorithm to evaluate the probability of generating these tree structures by every website model. Because of the large amount of pages in a website, we use a sampling technique that not only reduces the running time of the algorithm but also improves the accuracy of the classification process. At the end of this paper, we provide some experimental results which show the performance of our algorithm compared to the previous ones.
    Advances in Knowledge Discovery and Data Mining, 13th Pacific-Asia Conference, PAKDD 2009, Bangkok, Thailand, April 27-30, 2009, Proceedings; 01/2009