this paper describes an interactive graphical user interface tool called Visual Apriori that can be used to study two famous frequent itemset generation algorithms, namely, Apriori and Eclat. Understanding the functio...
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ISBN:
(纸本)0769524958
this paper describes an interactive graphical user interface tool called Visual Apriori that can be used to study two famous frequent itemset generation algorithms, namely, Apriori and Eclat. Understanding the functional behavior of these two algorithms is critical for students taking a data mining course;and Visual Apriori provides a hands-on environment for doing so. Visual Apriori relies on active participation from the user where one inputs a transactional database and the tool produces a tree-based frequent itemset generation animation for the algorithm chosen. Visual Apriori provides an effortless learning experience by featuring user-friendly and easy to understand controls.
Understanding contextual behavior is very important in order to develop a context-aware retrieval system. this paper discusses the philosophy behind the development of the "Evolutionary Behavior Of Textual Semant...
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ISBN:
(纸本)0769524958
Understanding contextual behavior is very important in order to develop a context-aware retrieval system. this paper discusses the philosophy behind the development of the "Evolutionary Behavior Of Textual Semantics" (EBOTS) system. the EBOTS system is retrieval oriented knowledge representation and management system. this paper proposes a formal model of correlation that can be combined with traditional local and global weighing schemes. Intuitive contextual behavior is studied as apart of proposed research work. Context retrieval based on semantic knowledge allows abstract queries to be defined, instead of exact word-based queries. the results of the context retrieval for a classic3 and Time dataset using the EBOTS system have been discussed in this paper. the paper makes a contribution to the semantic knowledge representation and retrieval algorithms.
In these days, the data are being more and more important for not only social or commercial aspects but also military and security aspects. therefore, storing the data accurately and accessing it exactly are quite imp...
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ISBN:
(纸本)9781509002870
In these days, the data are being more and more important for not only social or commercial aspects but also military and security aspects. therefore, storing the data accurately and accessing it exactly are quite important issues. Currently, most databases use 3 dimension (3D) data structure to store the physical parameters of real objects, which are width, length and depth/height. If the data have the four dimension for any object, it will definitely be more useful than 3D structure. In this paper, we investigated to how the time can be used as the 4th dimension for any object and the concepts of dynamic calculation of the time in order to store it in databases. Some type of objects have been selected as base shapes such as rectangular, cylinder, sphere, ellipse, pyramid and cone, for 4th dimension objects and a sample application is given in the study in order to explain how the time dimension can be used for databases.
this paper takes a System of Systems (SoS) approach to the realization of machine intelligence. It is a case study pertaining to the symbolic design of a Freon refrigeration device, which is subsequently transformed u...
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We extend soft nearest neighbor classification to fuzzy classification with adaptive class labels. the adaptation follows a gradient descent on a cost function. Further, it is applicable for general distance measures,...
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Individualization of drug delivery in treatment of chronic ailments is a challenge to the physician. Variability of response across patient population requires tailoring the dosing strategies to individual's needs...
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Predicting cellular properties from molecular or genetic data is a challenge for bioinformatics and machinelearning. In brain slices of neuronal tissue, it has become possible to both measure electro-physiological pr...
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ISBN:
(纸本)0769524958
Predicting cellular properties from molecular or genetic data is a challenge for bioinformatics and machinelearning. In brain slices of neuronal tissue, it has become possible to both measure electro-physiological properties of a given neuron and to extract a sample of its cytoplasm so that expressed genes can be amplified. thus, the presence or absence of genes related to ion channels in the neuronal cell membrane can be correlated with neuronal behavior encoded as a set of electro-physiological parameters. A typical gene amplification process is asymmetric in the sense that false positives are very rare, whereas false negatives (genes expressed but not amplified) are rather common. An analysis of a probabilistic model of that process yields a similarity measure between two strings of amplified genes that takes the asymmetry of the amplification process into account. this similarity measure can be put under the form of a conformal-transformed kernel. We provide experiments with support-vector machines on artificial and neuronal data.
We propose thai Sign to thai machine Translation (TSTMT), an alternative approach to machine translation which is used for translating from thai sign language into thai text, TSTMT performs the translation by recogniz...
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In this paper, we are proposing a combination scheme of kernels information of Support Vector machines (SVMs) for improved classification task using Genetic Programming. In the scheme, first, the predicted information...
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this paper describes the use of a mixture of abduction and induction for the temporal modelling of the effects of toxins in metabolic networks. Background knowledge is used which describes network topology and functio...
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ISBN:
(纸本)0769524958
this paper describes the use of a mixture of abduction and induction for the temporal modelling of the effects of toxins in metabolic networks. Background knowledge is used which describes network topology and functional classes of enzymes. this background knowledge, which represents the present state of understanding, is incomplete. In order to overcome this incompleteness hypotheses are considered which consist of a mixture of specific inhibitions of enzymes (ground facts) together with general (non-ground) rules which predict classes of enzymes likely to be inhibited by the toxin. the foreground examples were derived from in vivo experiments involving NMR analysis of time-varying metabolite concentrations in rat urine following injections of toxin. Hypotheses about inhibition are built using the Inductive Logic Programming system Progol5.0 and predictive accuracy is assessed for boththe ground and the non-ground cases.
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