• Journal of Internet Computing and Services
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• v.7 no.4
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• pp.91-99
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• 2006

The incremental parsing technique plays an important role in language-based environment which allows the incremental construction of a program. It improves the performance of a system by reanalyzing only the changed part of a program. The conventional incremental parsing uses the stack data structure in order to store the parsing information. In this paper, we suggest a threaded tree construction algorithm which parse by adding the threaded node address instead of using a stack data structure. We also suggest an incremental threaded tree construction which has incremental parsing process of five steps using the constructed threaded tree.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.9-17
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• 1994

The incremental parsing techniques are essential part of language-based environment which allow incremental construction of programs, as reparsing of modified part of the program alone enhances the system performance. A number of methods and algorithms have been proposed for incremental parsing and for the construction of incremental parsers. The purpose of this paper is to review the earlier incremental parsing algorithms which are too expensive in both time and space, and to present an efficient incremental LR parsing algorithm which is more efficient than the previous ones. In this paper, we generate automatically an extended LR parsing tables which allow the nonterminal symbols as well as terminal symbols for the input, and apply them to our efficient incremental LR parsing algorithm. Using these extended LR parsing tables. we suggest several methods to reduce its memory spaces and parsing steps as well. The algorithms described here were implemented in C language and YACC on a SUN UNIX OS, and were tested with several grammars and strings.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.258-267
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• 2013

New incremental learning algorithm using extended data expression, based on probabilistic compounding, is presented in this paper. Incremental learning algorithm generates an ensemble of weak classifiers and compounds these classifiers to a strong classifier, using a weighted majority voting, to improve classification performance. We introduce new probabilistic weighted majority voting founded on extended data expression. In this case class distribution of the output is used to compound classifiers. UChoo, a decision tree classifier for extended data expression, is used as a base classifier, as it allows obtaining extended output expression that defines class distribution of the output. Extended data expression and UChoo classifier are powerful techniques in classification and rule refinement problem. In this paper extended data expression is applied to obtain probabilistic results with probabilistic majority voting. To show performance advantages, new algorithm is compared with Learn++, an incremental ensemble-based algorithm.

• Journal of Internet Computing and Services
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• v.2 no.3
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• pp.71-79
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• 2001

In Incremental attribute evaluation algorithm, a new input attribute is exact1y compared with a previous input attribute tree, and then determine which subtrees from the old should be used in constructing the new one. In this paper incremental attribute evaluation algorithm was used to make incremental evlauation of hierarchical attribute grammar more efficient1y, and reconstructing the incremental attribute evaluation algorithm by analyzing that of Carle and Pollock, finally the incremental attribute evaluation algorithm for optimalized attribute tree d' copy was constructed by applying element of attribute !ree dcopy to a new attribute tree d' copy. Also proving that how the reused nod and type of defined parameter in input program carried out the incremental attribute evaluation by using that algorithm.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.207-211
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• 2023

Traditional methods for datamining typically assume that the data is small, centralized, memory resident and static. But this assumption is no longer acceptable, because datasets are growing very fast hence becoming huge from time to time. There is fast growing need to manage data with efficient mining algorithms. In such a scenario it is inevitable to carry out data mining in a distributed environment and Frequent Itemset Mining (FIM) is no exception. Thus, the need of an efficient incremental mining algorithm arises. We propose the Distributed Incremental Approximate Frequent Itemset Mining (DIAFIM) which is an incremental FIM algorithm and works on the distributed parallel MapReduce environment. The key contribution of this research is devising an incremental mining algorithm that works on the distributed parallel MapReduce environment.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.381-383
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• 2003

The authors have developed a solution method based on the incremental algorithm. The optimal load balance between feeders is found through the incremental algorithm with allowing the loop configuration. Therefore loops can be configured in the resulting configuration, and then we must eliminate all loops to satisfy radiality of distribution system because the distribution system is operated under radial status. But the optimal load balance through incremental algorithm is deteriorated when the loops are eliminated. In this paper, authors propose a new method which is to improve the searching procedure of path in the incremental algorithm, that is, so as not to make loop in the path configuration.

• The KIPS Transactions:PartA
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• v.8A no.3
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• pp.209-216
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• 2001

The effective way to apply incremental attribute grammar to a complex language process is the use of optimized algorithm. In optimized algorithm for incremental attribute grammar, the new input attribute tree should be exactly compared with the previous input attribute tree, in order to determine which subtrees from the old should be used in constructing the new one. In this paper the new optimized algorithm was reconstructed by analyzing the algorithm suggested by Carle and Pollock, and a generation process of new attribute tree d’copy was added. Through the performance evaluation for the suggested matching algorithm, the run time is approximately improved by 19.5%, compared to the result of existing algorithm.

• Journal of Information Processing Systems
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• v.6 no.2
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• pp.163-176
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• 2010

Nowadays due to the rapid advances in the field of information systems, transactional databases are being updated regularly and/or periodically. The knowledge discovered from these databases has to be maintained, and an incremental updating technique needs to be developed for maintaining the discovered association rules from these databases. The concept of Temporal Association Rules has been introduced to solve the problem of handling time series by including time expressions into association rules. In this paper we introduce a novel algorithm for Incremental Mining of General Temporal Association Rules (IMTAR) using an extended TFP-tree. The main benefits introduced by our algorithm are that it offers significant advantages in terms of storage and running time and it can handle the problem of mining general temporal association rules in incremental databases by building TFP-trees incrementally. It can be utilized and applied to real life application domains. We demonstrate our algorithm and its advantages in this paper.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.6
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• pp.579-583
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• 2012

The purpose of this paper is to suggest IDE (Incremental Displacement Estimation) algorithm for the previously proposed visually servoed paired structured light system. The system is composed of two sides facing with each other, each with one or two lasers with a 2-DOF manipulator, a camera, and a screen. The 6-DOF displacement between two sides can be estimated by calculating the positions of the projected laser beams and rotation angles of the manipulators. In the previous study, Newton-Raphson or EKF (Extended Kalman Filter) has been used as an estimation algorithm. Although the various experimental tests have validated the performance of the system and estimation algorithms, the computation time is relatively long since aforementioned algorithms are iterative methods. Therefore, in this paper, a non-iterative incremental displacement estimation algorithm which updates the previously estimated displacement with a difference of the previous and the current observed data is introduced. To verify the performance of the algorithm, experimental tests have been performed. The results show that the proposed non-iterative algorithm estimates the displacement with the same level of accuracy compared to the EKF with multiple iterations with significantly less computation time.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.28A no.1
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• pp.60-73
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• 1991

This paper presents an efficient algorithm for incremental and hierarchical design rule checking of VLSI layouts, and describes the implementation of a layout editor using the proposed algorithm. Tracing the sliced edges divided by the intersection of the edges either ina polygon or in two polygons (Sliced-Edge Trace), the algorithm performs VLSI pattern operations like resizing and other Boolean operations. The algorithm is not only fast enough to check the layouts of full-custom designs in real-time, but is general enough to be used for arbitrarily shaped polygons. The proposed algorithm was employed in developingt a layout editor on engineering workstations running UNIX. The editor has been successfully used for checking, generating and resizing of VLSI layouts.