124 research outputs found

    An Efficient Fuzzy Possibilistic C-Means with Penalized and Compensated Constraints

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    Improvement in sensing and storage devices and impressive growth in applications such as Internet search, digital imaging, and video surveillance have generated many high-volume, high-dimensional data. The raise in both the quantity and the kind of data requires improvement in techniques to understand, process and summarize the data. Categorizing data into reasonable groupings is one of the most essential techniques for understanding and learning. This is performed with the help of technique called clustering. This clustering technique is widely helpful in fields such as pattern recognition, image processing, and data analysis. The commonly used clustering technique is K-Means clustering. But this clustering results in misclassification when large data are involved in clustering. To overcome this disadvantage, Fuzzy- Possibilistic C-Means (FPCM) algorithm can be used for clustering. FPCM combines the advantages of Possibilistic C-Means (PCM) algorithm and fuzzy logic. For further improving the performance of clustering, penalized and compensated constraints are used in this paper. Penalized and compensated terms are embedded with the modified fuzzy possibilistic clustering method2019;s objective function to construct the clustering with enhanced performance. The experimental result illustrates the enhanced performance of the proposed clustering technique when compared to the fuzzy possibilistic c-means clustering algorithm

    Outline of a new feature space deformation approach in fuzzy pattern recognition

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    Sposobnost prepoznavanja oblika je jedno od najznačajnijih svojstava koja karakterišu inteligentno ponašanje bioloških ili veštačkih sistema. Matematičko prepoznavanje oblika predstavlja formalnu osnovu za rešavanje ovog zadatka primenom precizno forumulisanih algoritama, koji su u najvećem delu bazirni na konvencionalnoj matematici. Kod kompleksnih sistema ovakav pristup pokazuje značajne nedostatke, prvenstveno zbog zahteva za obimnim izračunavanjima i nedovoljne robusnosti. Algoritmi koji su bazirani na 'soft computing' metodama predstavljaju dobru alternativu, otvarajući prostor za razvoj efikasnih algoritama za primenu u realnom vremenu, polazeći od činjenice da značenje sadržaja informacija nosi veću vrednost u odnosu na preciznost. U ovom radu izlaže se modifikacija i proširenje 'Subrtactive Clustering' metode, koja se pokazala efikasnom u obradi masivnih skupova oblika u realnom vremenu. Novi pristup koji je baziran prvenstveno na povezivanju parametara algoritma sa informacionim sadržajem prisutnim u skupu oblika koji se obrađuje, daje dodatne stepene slobode i omogućava da proces prepoznavanja bude vođen podacima koji se obrađuju. Predloženi algoritam je verifikovan velikim brojem simulacionih eksperimenata, od kojih su neki navedeni u ovom radu.Pattern recognition ability is one of the most important features that characterize intelligent behavior of either biological or artificial systems. Mathematical pattern recognition is the way to solve this problem using transparent algorithms that are mostly based on conventional mathematics. In complex systems it shows inadequacy, primary due to the needs for extensive computation and insufficient robustness. Algorithms based on soft computing approach offer a good alternative, giving a room to design effective tools for real-time application, having in mind that relevance (significance) prevails precision in complex systems. In this article is modified and extended subtractive clustering method, which is proven to be effective in real-time applications, when massive pattern sets is processed. The new understanding and new relations that connect parameters of the algorithm with the information underlying the pattern set are established, giving on this way the algorithm ability to be data driven to the maximum extent. Proposed algorithm is verified by a number of experiments and few of them are presented in this article

    Outline of a new feature space deformation approach in fuzzy pattern recognition

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    Sposobnost prepoznavanja oblika je jedno od najznačajnijih svojstava koja karakterišu inteligentno ponašanje bioloških ili veštačkih sistema. Matematičko prepoznavanje oblika predstavlja formalnu osnovu za rešavanje ovog zadatka primenom precizno forumulisanih algoritama, koji su u najvećem delu bazirni na konvencionalnoj matematici. Kod kompleksnih sistema ovakav pristup pokazuje značajne nedostatke, prvenstveno zbog zahteva za obimnim izračunavanjima i nedovoljne robusnosti. Algoritmi koji su bazirani na 'soft computing' metodama predstavljaju dobru alternativu, otvarajući prostor za razvoj efikasnih algoritama za primenu u realnom vremenu, polazeći od činjenice da značenje sadržaja informacija nosi veću vrednost u odnosu na preciznost. U ovom radu izlaže se modifikacija i proširenje 'Subrtactive Clustering' metode, koja se pokazala efikasnom u obradi masivnih skupova oblika u realnom vremenu. Novi pristup koji je baziran prvenstveno na povezivanju parametara algoritma sa informacionim sadržajem prisutnim u skupu oblika koji se obrađuje, daje dodatne stepene slobode i omogućava da proces prepoznavanja bude vođen podacima koji se obrađuju. Predloženi algoritam je verifikovan velikim brojem simulacionih eksperimenata, od kojih su neki navedeni u ovom radu.Pattern recognition ability is one of the most important features that characterize intelligent behavior of either biological or artificial systems. Mathematical pattern recognition is the way to solve this problem using transparent algorithms that are mostly based on conventional mathematics. In complex systems it shows inadequacy, primary due to the needs for extensive computation and insufficient robustness. Algorithms based on soft computing approach offer a good alternative, giving a room to design effective tools for real-time application, having in mind that relevance (significance) prevails precision in complex systems. In this article is modified and extended subtractive clustering method, which is proven to be effective in real-time applications, when massive pattern sets is processed. The new understanding and new relations that connect parameters of the algorithm with the information underlying the pattern set are established, giving on this way the algorithm ability to be data driven to the maximum extent. Proposed algorithm is verified by a number of experiments and few of them are presented in this article

    Evaluating student behaviour on the MathE Platform - clustering algorithms approaches

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    The MathE platform is an online educational platform that aims to help students who struggle to learn college mathematics as well as students who wish to deepen their knowledge on subjects that rely on a strong mathematical background, at their own pace. The MathE platform is currently being used by a significant number of users, from all over the world, as a tool to support and engage students, ensuring new and creative ways to encourage them to improve their mathematical skills. This paper is addressed to evaluate the students’ performance on the Linear Algebra topic, which is a specific topic of the MathE platform. In order to achieve this goal, four clustering algorithms were considered; three of them based on different bio-inspired techniques and the k-means algorithm. The results showed that most students choose to answer only basic level questions, and even within that subset, they make a lot of mistakes. When students take the risk of answering advanced questions, they make even more mistakes, which causes them to return to the basic level questions. Considering these results, it is now necessary to carry out an in-depth study to reorganize the available questions according to other levels of difficulty, and not just between basic and advanced levels as it is.FCT - Fundação para a Ciência e a Tecnologia(2021-1-PT01-KA220-HED-000023288)This work has been supported by FCT Fundação para a Ciência e Tecnologia within the R&D Units Project Scope UIDB/00319/2020 and UIDB/05757/2020. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/202
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