Abstract Data Mining is one of the most significant tools for discovering association patterns for many knowledge domains. Yet, there are deficits of current data-mining techniques, i.e.: 1) current methods are based on plane-mining using pre-defined schemata so that a re-scanning of the entire database is required whenever new attributes are added. 2) An association rule may be true on a... Data Mining is one of the most significant tools for discovering association patterns for many knowledge domains. Yet, there are deficits of current data-mining techniques, i.e.: 1) current methods are based on plane-mining using pre-defined schemata so that a re-scanning of the entire database is required whenever new attributes are added. 2) An association rule may be true on a certain granularity but false on a smaller ones and vise verse. 3) Existing methods can only find either frequent rules or infrequent rules, but not both at the same time. This paper proposes a novel algorithm alone with a data structure that together solves the above weaknesses at the same time. Thus, the proposed approach can improve the efficiency and effectiveness of related data mining approach. By means of the data structure, we construct a forest of concept taxonomies which can be applied for representing the knowledge space. On top of the concept taxonomies, the data mining is developed as a compound process to find the large-itemsets, to generate, to update and to output the association patterns that can represent the composition of various taxonomies. This paper also derived a set of benchmarks to demonstrate the level of efficiency and effectiveness of the data mining algorithm. Last but not least, this paper presents the experimental results with respect to efficiency, scalability, information loss, etc. of the proposed approach to prove its advantages. Read More Read Less

Abstract An important issue to be solved in object recognition is how to represent an object so that a computer vision system is able to recognize it regardless of their shapes, sizes, position and orientation. In this paper we propose a shape based image retrieval based on Zernike Moments as shape descriptor for retrieval of single binary object from image database.... An important issue to be solved in object recognition is how to represent an object so that a computer vision system is able to recognize it regardless of their shapes, sizes, position and orientation. In this paper we propose a shape based image retrieval based on Zernike Moments as shape descriptor for retrieval of single binary object from image database. To test the robustness of Zernike Moments, MPEG-7 image database is used. Our experimental investigations confirm that Zernike Moment properties are invariant to rotation, and translation with low sensitivity to noise, however is not invariant to scale. Read More Read Less

Abstract Data Mining is getting increasingly important for discovering association patterns for health service innovation and Customer Relationship Management (CRM) etc. Yet, there are deficits of existing data mining techniques. Since most of them perform a plain mining based on predefined schemata through the data warehouse as a whole, a re-scan must be done whenever new attributes are added. Secondly, an... Data Mining is getting increasingly important for discovering association patterns for health service innovation and Customer Relationship Management (CRM) etc. Yet, there are deficits of existing data mining techniques. Since most of them perform a plain mining based on predefined schemata through the data warehouse as a whole, a re-scan must be done whenever new attributes are added. Secondly, an association rule may be true on a certain granularity but fail on a smaller one and vise verse. Last but not least, they are usually designed to find either frequent or infrequent rules. After a survey of a category of significant health services, we propose a data mining algorithm alone with a forest data structure to solve aforementioned weaknesses at the same time. At first, we construct a forest structure of concept taxonomies that can be used for representing the knowledge space. On top of it, the data mining is developed as a compound process to find the large-itemsets, to generate, to update and to output association rules that can represent services portfolio. After a set of benchmarks derived to measure the performance of data mining algorithms, we present the performance with respect to efficiency, scalability, information loss, etc. The results show that the proposed approach is better than existing methods with regard to the level of efficiency and effectiveness. Read More Read Less

Abstract User’s tasks, habits and preferences require ubiquitous and mobile context aware interactive system. Disable people find themselves excluded from services that can be offered by pervasive system. It is essential to take into account all categories of user with disability in the design and development of computer systems in order to provide opportunity to make their daily activities easily and... User’s tasks, habits and preferences require ubiquitous and mobile context aware interactive system. Disable people find themselves excluded from services that can be offered by pervasive system. It is essential to take into account all categories of user with disability in the design and development of computer systems in order to provide opportunity to make their daily activities easily and simple. The problem is so complex when the context of use changes dynamically over time. The computing system becomes more confronted to a need to be adapted to multiple configurations of the context of use. The present paper describes a position paper relative to recently existing works that deals with context awareness applications as a way of adaptation process in order to provide a global vision and to deduce the architecture of context aware system supporting adaptation. Read More Read Less