32240142
Issues in modeling for data mining
Modeling in data mining has not been fully explored; so data mining is often regarded as a set of added operations in database systems. The consequence is that many notions are overloaded. Some terms often have different semantics. This paper continues previous effort to explore and reiterate some fundamental issues in data mining.
206617892
Granular Computing
The structure and representation theories of (crisp/fuzzy) granulations are presented. The results are applied to data mining, fuzzy control, security and etc.
9338940
Information flows in e-government
Classical MAC and DAC are reexamined in the light of e-government. In this paper, we focus on the information flows, which have been addressed by classical MAC, in my view, somewhat inadequately, and never been addressed by classical DAC.
5090614
Fuzzy Reasoning and Rough Sets
Fuzzy concepts are represented by fuzzy sets, so a fuzzy knowledge is defined to be a collection of fuzzy sets (concepts) which includes two constants, 1 (true) and 0 (false). Abstractly, a logical derivation is a finite series of logical operations acting on a given knowledge. In fuzzy world, logical operations are defined by mathematical operations. So fuzzy logical derivations are mathematical derivations, and vice versa In this paper, the closure of fuzzy reasoning is characterized by fuzzy rough sets, and by topologies (neighborhood systems). A new fuzzy concept F N is derivable from the old knowledge K iff the membership function of F N is continuous in K-topology, or equivalently, iff F N is a rough fuzzy set (concept) of I N D(K),or plainly, iff the membership function of F N is a “step” function in the sense that it takes a constant value in each equivalence class of I N D(K).A fuzzy set L partitions the universe into equivalence classes; each equivalence class consists of those elements which have the same degree of membership. The indiscernibility relation I N D(K)over K is the “intersection” of all the equivalence relations induced by the fuzzy sets in K. The membership function of a fuzzy set is a real valued function, so U can be given the minimal topology such that the membership function of each fuzzy set in K is a continuous function. Such topology is called K-topology.
19867080
Context-Free Fuzzy Sets in Data Mining Context
Conventional studies on rule discovery and rough set methods mainly focus on acquisition of rules, the targets of which have mutually exclusive supporting sets. However, mutual exclusiveness does not always hold in real-world databases, where conventional probabilstic approaches cannot be applied. In this paper, first, we show that these phenomena are easily found in data mining contexts: when we apply attribute-oriented generalization to attributes in databases, generalized attributes will have fuzziness for classification. Secondly, we show that real-world databases may have fuzzy contexts. Then, finally, these contexts should be analyzed by using fuzzy techniques, where context-free fuzzy sets will be a key idea.
6446139
A semi-supervised efficient learning approach to extract biological relationships from web-based biomedical digital library
Many biological results are published only in plain --text documents and these documents or their abstracts are collected in web-based digital libraries such as PubMed and BioMed Central. To expedite the progress of functional bioinformatics, it is important to efficiently process large amounts of these documents, to extract these results into a structured format, and to store them in a database so that these results can be retrieved and analyzed by biologists and medical researchers. Automated discovery and extraction of the biological knowledge from biomedical web documents has become essential because of the enormous amount of biomedical literature published each year. In this paper we present a semi-supervised efficient learning approach to automatically extract biological knowledge from the web-based digital libraries. Our method integrates ontology-based semantic tagging as well as information extraction and data mining together. Our method automatically learns the patterns based on a few user seed tuples and then extracts new tuples from the biomedical web documents based on the discovered patterns. A novel system, SPIE Scalable and Portable Information Extraction, is implemented and tested on the PuBMed to find the chromatin protein --protein interaction. The experimental results indicate our approach is very effective in extracting biological knowledge from a huge collection of biomedical web documents.
13328190
A β-expectation tolerance interval for general balanced mixed linear models
A @b-expectation tolerance interval procedure is derived from the concept of generalized pivotal quantity, which has been frequently used to obtain confidence intervals in situations where standard procedures do not lead to useful solutions. The proposed procedure can be applied to general balanced mixed linear models. Some practical examples are given to illustrate the proposed procedure. In addition, detailed simulation studies are conducted to evaluate its performance, showing that it can be recommended for use in practical applications.
23641664
Mediating Organizational Innovation: A Study of Implementation of Digital Technologies in TV News
Successful implementation of IT innovations requires mutual adjustment of technical features and social practices and structures of an organization. In this paper, we develop a synthesized model for mediating organizational innovation, drawn from Roger's model for organizational diffusion of innovation and Orlikowski et al.'s (2004) model for technology use mediation, to investigate the implementation of digital technologies in TV news. Our analysis of two case studies of TV news stations highlights the phases of mediation that occurred in these organizations and points to differences in mediation styles from previous studies. We suggest that scope of the implementation and temporal pressure in the work context were important dimensions in the mediation process
14241577
How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies
Learning long-term temporal dependencies with recurrent neural networks can be a difficult problem. It has recently been shown that a class of recurrent neural networks called NARX networks perform much better than conventional recurrent neural networks for learning certain simple long-term dependency problems. The intuitive explanation for this behavior is that the output memories of a NARX network can be manifested as jump-ahead connections in the time-unfolded network. These jump-ahead connections can propagate gradient information more efficiently, thus reducing the sensitivity of the network to long-term dependencies. This work gives empirical justification to our hypothesis that similar improvements in learning long-term dependencies can be achieved with other classes of recurrent neural network axchitectures simply by increasing the order of the embedded memory. In particular we explore the impact of learning simple long-term dependency problems on three classes of recurrent neural network architectures: globally recurrent networks, locally recurrent networks, and NARX (output feedback) networks.Comparing the performance of these architectures with different orders of embedded memory on two simple long-term dependencies problems shows that all of these classes of network architectures demonstrate significant improvement on learning long-term dependencies when the orders of embedded memory are increased. These results can be important to a user comfortable with a specific recurrent neural network architecture because simply increasing the embedding memory order of that architecture will make it more robust to the problem of long-term dependency learning.
31273280
Adaptive-Hierarchical-Filtering Technique for Image Enhancement
This paper describes a novel adaptive-hierarchical-filtering technique to achieve high-quality image enhancement when the image possesses the artifact of moire pattern during the reproduction of the image by different computer peripherals such as color copier, or scanners plus printers.Commercial magazine images are halftoned images. Unacceptable noises and moire distortion may result when halftone images are copied (i.e., scanned and printed). In this paper, we analyze the formation of moire patterns in the frequency and spatial domain. Based on the analysis, a set of hierarchical filter is developed to suppress the moire artifacts and enhance the image adaptively. The hierarchical filter consists of a set of variable-length low-pass filters and high-pass filters. The low-pass filters have a nice inheritance of canceling aliased low frequency components (moire distortion). High-pass filtering is also applied to sharpen image edges. An image classifier is developed to determine that an edge is either a global true edge (for sharpening enhancement) or a local halftone's micro-structural edge (for LPF for moire reduction), so that adaptive filter technique can be applied to achieve the smooth transition between sharp edges and smooth halftone regions. Thus, we achieves overall high-quality output images.Experimental results have been shown the effectiveness of the presented technique that works well on wide combinations of above-mentioned 6 factors, which will be explained during the following sections, for high-quality magazine image reproduction.
26234971
A Color Consistency Algorithm Between Different Printers
Color consistency between different color peripherals su as printers, scanners and digital cameras is a well-kno problem. In this paper, a color consistency algorith between different printers is proposed. Each printer has its own characteristics. The co behavior of a printer can be modeled by a nonline characteristic function. We approximate the characteris function by a three-dimensional look-up table. Then, transfer function between two printers can be construc based on their characteristic functions. The transfer funct is to compensate one’s color behavior and simula another’s color behavior. After applying the transfe function, the color difference ( ∆E) and hue difference can be reduced to two to three times smaller.