انجمن کامپیوتر ایران

كاوش داده هايي مانند ميكروآرايه ها كه ابعاد بس يار بالا يي دارند، باعث به وجود آمدن مشكلات جد ي بر اي اكثر الگور يتم ه اي كارآمد الگوكاوي موجود مي گردد و كارآ يي آنها را با چالش جد ي مواجه مي نمايد. در سالهاي اخير متدهاي جديدي ارائه شده كه مبتني بر شمارش سطرها هستند و با انجام يك جستجوي از پايين به بالا بر روي فضاي تركيب سطرها سعي در الگوكاوي كارآمد دارند كه با توجه به تعداد اندك سطرها در داده هاي ميكروآرايه اي، اين روش ها بس يار كارآمدتر از روش هاي مبتني بر شمارش ستون ها هستند. اما رويكرد از پايين به بالاي اين روش ها از يك سو و عدم امكان فشرده سازي مناسب اطلاعات از سوي ديگر سبب شده است كه روش هاي مزبور فاقد كارآيي كافي به منظور هرس نمودن فضاي جس تجو با توجه به مقدار حد آستانه مشخص شده توسط كاربر باشند و اين مساله منجر به طولاني شدن زمان اجرا و ايجاد فضاي سربار در حافظه مي گردد. در اين مقاله روش جستجوي جديدي به صورت كاوش از بالابه پا يين مبتني بر نمايش بيتي تراكنش ها بر روي ي ك درخت شمارش سطر ارائه مي گردد كه امكان استفاده كامل از قدرت هرس كردن توسط حد آستانه اي داده شده توسط كاربر را فراهم مي نمايد و به كوچك شدن شگفت انگيز فضاي جستجو مي انجامد . با استفاده از ا ين استراتژ ي جستجو، الگوريتم كاوشي به نام Clobimat طراحي شده است كه قادر به يافتن مجموعه كامل همه الگوه اي تكرارشونده بسته در مجموعه داده هاي با ابعاد بسيار بالاست. به علاوه روش جديدي براي تست بسته بودن الگوها ارائه شده است كه مانع از پويش چندين باره مجموعه داده مي گردد. مطالعات انجام شده نشان داده است كه الگوريتم ارائه شده در اين مقاله به لحاظ كارآيي برتري قابل ملاحظه اي نسبت به الگور يتم Carpenter كه يك الگور يتم جستجوي پايين به بالا ي فضاي سطرهاست- و الگوريتم FPClose كه يك الگوريتم مبتني بر جستجوي ستون هاست- دارد.

In recent years many researches have focused on ad-hoc networks, mainly because of their independence to any specific structure. These networks suffers from frequent and rapid topology changes that cause many challenges in their routing. Most of the routing protocols try to find a path between source and destination nodes because any path will expire, offer a short period, the path reconstruction may cause the network inefficiency. The proposed protocol build two paths between source and destination and create backup paths during the route reply process, route maintenance process and local recovery process in order to improve the data transfer and the fault tolerance. The protocol performance is demonstrated by using the simulation results obtain from the global mobile simulation software(Glomosim). The experimental results show that this protocol can decrease the packet loss ratio rather than DSR and SMR and it is useful for the applications that need a high level of reliability.

Blinded data mining is a branch of data mining technique which is focused on protecting user privacy. To mine sensitive data such as medical information, it is desirable to protect privacy and there is not worry about revealing personalized data. In this paper a new approach for blinded data mining is suggested. It is based on ontology and k-anonymity generalization method. Our method generalizes a private table by considering table fields’ ontology, so that each tuple will become k-anonymous and less specific to not reveal sensitive information. This method is implemented using protégé and java for evaluation.

This paper presents an efficient algorithm for adaptive binarization of degraded document images. Document binarization algorithms suffer from poor and variable contrast in document images. We propose a contrast independent binarization algorithm that does not require any parameter setting by user. Therefore, it can handle various types of degraded document images. The proposed algorithm involves two consecutive stages. At the first stage, independent of contrast between foreground and background, some parts of each character are extracted and in the second stage, the gray level of foreground and background are locally estimated. For each pixel, the average of estimated foreground and background gray levels is defined as threshold. After extensive experiments, the proposed binarization algorithm demonstrate superior performance against four well-know binarization algorithms on a set of degraded document images captured with camera.

Quantum Evolutionary Algorithm (QEA) is a novel optimization algorithm which uses a probabilistic representation for solution and is highly suitable for combinatorial problems like Knapsack problem. Fractal image compression is a well-known problem which is in the class of NP-Hard problems. Genetic algorithms are widely used for fractal image compression problems, but QEA is not used for this kind of problems yet. This paper uses a novel Functional Sized population Quantum Evolutionary Algorithm for fractal image compression. Experimental results show that the proposed algorithm has a better performance than GA and conventional fractal image compression algorithms.

Increasing the domain of locality by using treeadjoining- grammars (TAG) encourages some researchers to use it as a modeling formalism in their language application. But parsing with a rich grammar like TAG faces two main obstacles: low parsing speed and a lot of ambiguous syntactical parses. We uses an idea of the shallow parsing based on a statistical approach in TAG formalism, named supertagging, which enhanced the standard POS tags in order to employ the syntactical information about the sentence. In this paper, an error-driven method in order to approaching a full parse from the partial parses based on TAG formalism is presented. These partial parses are basically resulted from supertagger which is followed by a simple heuristic based light parser named light weight dependency analyzer (LDA). Like other error driven methods, the process of generation the deep parses can be divided into two different phases: error detection and error correction, which in each phase, different completion heuristics applied on the partial parses. The experiments on Penn Treebank show considerable improvements in the parsing time and disambiguation process.

A model of episodic memory is derived to propose algorithms of text categorization with semantic space models. Performances of two algorithms are contrasted using textual material of the text-mining context ‘DEFT09’. Results confirm that the episodic memory metaphor provides a convenient framework to propose efficient algorithm for text categorization. One algorithm has already been tested with LSA. The present paper extends these algorithms to another model of Word Vector named Random Indexing.