15380898
Data Mining and Machine Oriented Modeling: A Granular Computing Approach
From the processing point of view, data mining is machine derivation of interesting properties (to human) from the stored data. Hence, the notion of machine oriented data modeling is explored: An attribute value, in a relational model, is a meaningful label (a property) of a set of entities (granule). A model using these granules themselves as attribute values (their bit patterns or lists of members) is called a machine oriented data model. The model provides a good database compaction and data mining environment. For moderate size databases, finding association rules, decision rules, and etc., can be reduced to easy computation of set theoretical operations of granules. In the second part, these notions are extended to real world objects, where the universe is granulated (clustered) into granules by binary relations. Data modeling and mining with such additional semantics are formulated and investigated. In such models, data mining is essentially a machine “calculus” of granules-granular computing.
7528349
Semantic based clustering of Web documents
A new methodology that structures the semantics of a collection of documents into the geometry of a simplicial complex is developed: a primitive concept is represented by a top dimension simplex, and a connected component represents a concept. Based on these structures, documents can be clustered into some meaningful classes. Experiments with three different data sets from web pages and medical literature have shown that the proposed unsupervised clustering approach performs significantly better than traditional clustering algorithms, such as k-means, AutoClass and hierarchical clustering (HAC). This abstract geometric model seems have captured the intrinsic semantics of the documents.
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.
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.
34363254
Feature Subset Selection Based on Relative Dependency between Attributes
Feature subset selection is an importent component of knowledge discovery and data mining systems to help reduce the data dimensionality. Rough sets theory provides a mechanism of selecting feature subsets. In the rough set community, most feature subset selection algorithms are attributes reduct-oriented; that is, finding minimum reducts of the conditional attributes of a decision table. Two main approaches to finding attribute reducts are categorized as discernibility functions-based and attribute dependency-based. These algorithms, however, suffer from intensive computations of either discernibility functions for the former or positive regions for the latter. In this paper, we propose a new concept, called relative attribute dependency, and present a sufficient and necessary condition of the minimum conditional attributes reduct of a decision table represented with the relative attribute dependency. The relative attribute dependency can be calculated by counting the distinct rows of the sub-decision table, instead of generating discernibility functions or positive regions. Thus the computation efficiency of minimum reducts are highly improved. We develop two algorithms for finding minimum reducts of the conditional attributes, one brute-force algorithm and the other heuristic algorithm using attribute entropy as the heuristic function. We also show the results of these algorithms by an illustrative example.
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.
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.
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
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.
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.