• Journal of Korea Multimedia Society
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• v.10 no.8
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• pp.945-957
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• 2007

Quantitative association rules apply a binary association to the data which have the relatively strong quantitative attributions in a large database system. When a domain range of quantitative data which involve the significant meanings for the association is too broad, a domain requires to be divided into a proper interval which satisfies the minimum support for the generation of large interval items. The reliability of formulated rules is enormously influenced by the generation of large interval items. Therefore, this paper proposes a new method to efficiently generate the large interval items. The proposed method does not lose any meaningful intervals compared to other existing methods, provides the accurate large interval items which are close to the minimum support, and minimizes the loss of characteristics of data. In addition, since our method merges data where the frequency of data is high enough, it provides the fast run time compared with other methods for the broad quantitative domain. To verify the superiority of proposed method, the real national census data are used for the performance analysis and a Clunix HPC system is used for the parallel processing.

• Journal of Auto-vehicle Safety Association
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• v.10 no.2
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• pp.29-35
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• 2018

This paper presents a quantitative evaluation method and result of moving vehicle perception using automotive radar. It is also important to analyze the accuracy of the perception algorithm quantitatively as well as to accurately percept nearby moving vehicles for safe and efficient autonomous driving. In this study, accuracy of the automotive radar-based perception algorithm which is developed based on interacting multiple model (IMM) has been verified via vehicle tests on real roads. In order to obtain experimental data for quantitative evaluation, Long Range Radar (LRR) has been mounted on the front of the ego vehicle and Short Range Radar (SRR) has been mounted on the rear side of both sides. RT-range has been installed on the ego vehicle and the target vehicle to simultaneously collect reference data on the states of the two vehicles. The experimental data is acquired in various relative positions and velocity, and the accuracy of the algorithm has been analyzed according to relative position and velocity. Quantitative analysis is conducted on relative position, relative heading angle, absolute velocity, and yaw rate of each vehicle.

• Journal of KIISE:Databases
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• v.35 no.2
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• pp.97-111
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• 2008

Commercial data mining tools basically support binary attributes only in mining association rules, that is, they can mine binary association rules only. In general, however. transaction databases contain not only binary attributes but also quantitative attributes. Thus, in this paper we propose a systematic approach to mine quantitative association rules---association rules which contain quantitative attributes---using commercial mining tools. To achieve this goal, we first propose an overall working framework that mines quantitative association rules based on commercial mining tools. The proposed framework consists of two steps: 1) a pre-processing step which converts quantitative attributes into binary attributes and 2) a post-processing step which reconverts binary association rules into quantitative association rules. As the pre-processing step, we present the concept of domain partition, and based on the domain partition, we formally redefine the previous bipartition and multi-partition techniques, which are mean-based or median-based techniques for bipartition, and are equi-width or equi-depth techniques for multi-partition. These previous partition techniques, however, have the problem of not considering distribution characteristics of attribute values. To solve this problem, in this paper we propose an intuitive partition technique, named standard deviation minimization. In our standard deviation minimization, adjacent attributes are included in the same partition if the change of their standard deviations is small, but they are divided into different partitions if the change is large. We also propose the post-processing step that integrates binary association rules and reconverts them into the corresponding quantitative rules. Through extensive experiments, we argue that our framework works correctly, and we show that our standard deviation minimization is superior to other partition techniques. According to these results, we believe that our framework is practically applicable for naive users to mine quantitative association rules using commercial data mining tools.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.269-278
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• 2007

Nowadays, from the study of quantitative linguistics, the application of quantitative method is located in a variety of fields as a necessary method. According to this phenomenon, the knowledge of statistical method is requisite for linguists. However, unfortunately, there still remain difficulties for them to acquire the statistical knowledge. So, it is needed for linguists to be helped by statisticians and their active roles. Accordingly, this study is going to emphasizing that statisticians should have more interests in the field of quantitative linguistics. Moreover, it will prove that by using statistical methods, analysis on the linguistic research becomes more objective and scientific.

• Proceedings of the Safety Management and Science Conference
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• 2011.11a
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• pp.213-217
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• 2011

The research shows misuse cases of quantitative regression analysis used in QC circle activity and six sigma movement which presents guidelines of correct use for quality practitioners. Additionally, the qualitative regression analysis that responses nonconforming ratio of variable y, is reviewed based on misuse cases for proper use by practitioners in the field. In most cases, there are frequent errors that involve the correlation analysis or ANOVA, regardless of using quantitative regression analysis. In addition, qualitative regression analysis for the nonconforming ratio that has dependent variable of discrete and categorical data, is often applied with quantitative regression and result in ineffective quality improvement.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1171-1171
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• 2001

Quantitative analysis is an important requirement in exploration, mining and processing of minerals. There is an increasing need for the use of quantitative mineralogical data to assist with bore hole logging, deposit delineation, grade control, feed to processing plants and monitoring of solid process residues. Quantitative analysis using X-Ray Powder Diffraction (XRD) requires fine grinding and the addition of a reference material, or the application of Rietveld analysis to XRD patterns to provide accurate analysis of the suite of minerals present. Whilst accurate quantitative data can be obtained in this manner, the method is time consuming and limited to the laboratory. Mid infrared when combined with multivariant analysis has also been used for quantitative analysis. However, factors such as the absorption coefficients and refractive index of the minerals requires special sample preparation and dilution in a dispersive medium, such as KBr to minimize distortion of spectral features. In contrast, the lower intensity of the overtones and combinations of the fundamental vibrations in the near infrared allow direct measurement of virtually any solid without special sample preparation or dilution. Thus Near Infrared Spectroscopy (NIR) has found application for quantitative on-line/in line analysis and control in a range of processing applications which include, moisture control in clay and textile processing, fermentation processes, wheat analysis, gasoline analysis and chemicals and polymers. It is developing rapidly in the mineral exploration industry and has been underpinned by the development of portable NIR spectrometers and spectral libraries of a wide range of minerals. For example, iron ores have been identified and characterized in terms of the individual mineral components using field spectrometers. Data acquisition time of NIR field instruments is of the order of seconds and sample preparation is minimal. Consequently these types of spectrometers have great potential for in-line or on-line application in the minerals industry. To demonstrate the applicability of NIR field spectroscopy for quantitative analysis of minerals, a specific example on the quantification of lateritic bauxites will be presented. It has been shown that the application of Partial Least Squares regression analysis (PLS) to the NIR spectra can be used to quantify chemistry and mineralogy in a range of lateritic bauxites. Important, issues such as sampling, precision, repeatability, and replication which influence the results will be discussed.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.207-210
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• 2003

In this paper, we explore a new data mining capability that is based on Quantitative Attribute and Time Series. Our solution procedure consists of two steps. First, We derive an algorithm to contain the Quantitative Attribute into a set of candidate item. Second, We redefine the concepts of confidence and support for composite association rules. It is shown that proposed methode is very advantageous and can lead to prominent performance improvement.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.210-214
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• 2002

Association rule mining is an exploratory learning task to discover some hidden dependency relationships among items in transaction data. Quantitative association rules denote association rules with both categorical and quantitative attributes. There have been several works on quantitative association rule mining such as the application of fuzzy techniques to quantitative association rule mining, the generalized association rule mining for quantitative association rules, and importance weight incorporation into association rule mining fer taking into account the users interest. This paper introduces a new method for generalized fuzzy quantitative association rule mining with importance weights. The method uses fuzzy concept hierarchies fer categorical attributes and generalization hierarchies of fuzzy linguistic terms fur quantitative attributes. It enables the users to flexibly perform the association rule mining by controlling the generalization levels for attributes and the importance weights f3r attributes.

• KIEAE Journal
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• v.8 no.5
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• pp.43-48
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• 2008

This study is about offering quantitatively analytic frame of streetscape, and also about making a way to be standardized and adapt changing landscape. This allows us to manage a form of elevation map after the application to GIS. The form of elevation map is a visible and vertical arrangement method of data recognizable while walking or driving. Applying measurable traits enables us to make a quantitative control over each element of which streetscape consists. After all, it would play a great roll in organizing and maintaining fine streetscape of each city. As the basic ways to make the elevation map, this study proposes the methods of providing quantitative analytic frame of streetscape after applying elevation data, Raster Data and Vector Data, which were investigated on the basis of GIS. In addition, as a simulation for increasing reality, certain streets, where the streetscape is very important, were chosen so that they enable us to utilize quantitatively analytic data of streetscape with analyzing the +degree of opening ratio in the boundary of D/H=2, comparing between wall area and windowpane area, comparing between facade area and sign board area, and calculating both area and ratio which are applied to ecospace.

• Toxicological Research
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• v.34 no.4
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• pp.303-310
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• 2018

The methods of applied genetic toxicology are changing from qualitative hazard identification to quantitative risk assessment. Recently, quantitative analysis with point of departure (PoD) metrics and benchmark dose (BMD) modeling have been applied to in vitro genotoxicity data. Two software packages are commonly used for BMD analysis. In previous studies, we performed quantitative dose-response analysis by using the PROAST software to quantitatively evaluate the mutagenicity of four piperidine nitroxides with various substituent groups on the 4-position of the piperidine ring and six cigarette whole smoke solutions (WSSs) prepared by bubbling machine-generated whole smoke. In the present study, we reanalyzed the obtained genotoxicity data by using the EPA's BMD software (BMDS) to evaluate the inter-platform quantitative agreement of the estimates of genotoxic potency. We calculated the BMDs for 10%, 50%, and 100% (i.e., a two-fold increase), and 200% increases over the concurrent vehicle controls to achieve better discrimination of the dose-responses, along with their BMDLs (the lower 95% confidence interval of the BMD) and BMDUs (the upper 95% confidence interval of the BMD). The BMD values and rankings estimated in this study by using the EPA's BMDS were reasonably similar to those calculated in our previous studies by using PROAST. These results indicated that both software packages were suitable for dose-response analysis using the mouse lymphoma assay and that the BMD modeling results from these software packages produced comparable rank orders of the mutagenic potency.