34342377
Database Mining on Derived Attributes
Selecting a desirable set of attributes is essential in data mining. In this paper, we develop the notion of universal model, on which a complete set of all possible derived attributes can be generated. Then, for any data mining task, the relation can select desirable attributes by interpreting them from the universal model. The development of the universal model is based on the relation lattice, which was initiated by T. T. Lee around 1983. However, we define the lattice differently. The relation lattice theory, as rough sets, is based on the partitions induced by attributes.
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.
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.
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.
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.
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
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.
29878206
An architecture for interoperable program understanding tools
Program understanding tools are currently not interoperable, leading researchers to waste significant resources reinventing already existing tools. Even commercial environments that have been designed to support the construction of program understanding tools have serious flaws in this regard. This paper discusses CORUM (Common Object-based Re-engineering Unified Model), an architecture to support interoperability between program understanding tools, and it provides several examples of CORUM's use in the construction of new tools for concept recognition and program visualization.
610912
Dynamic search in peer-to-peer networks
This work specifically addresses the search issues in unstructured peer-to-peer (P2P) systems that involve the design of an efficient search algorithm, the proposed dynamic search, and the modeling of P2P systems reflecting real measured P2P networks. Through simulations, we will show dynamic search outperforms other existing ones in terms of performance aspects.
12985850
Learning a class of large finite state machines with a recurrent neural network
Abstract One of the issues in any learning model is how it scales with problem size. The problem of learning finite state machine (FSMs) from examples with recurrent neural networks has been extensively explored. However, these results are somewhat disappointing in the sense that the machines that can be learned are too small to be competitive with existing grammatical inference algorithms. We show that a type of recurrent neural network (Narendra & Parthasarathy, 1990, IEEE Trans. Neural Networks, 1, 4–27) which has feedback but no hidden state neurons can learn a special type of FSM called a finite memory machine (FMM) under certain constraints. These machines have a large number of states (simulations are for 256 and 512 state FMMs) but have minimal order, relatively small depth and little logic when the FMM is implemented as a sequential machine.