• Journal of Korean Academy of Nursing
• /
• v.31 no.2
• /
• pp.328-339
• /
• 2001

The purpose of this study was to describe decision making model of 180 public health nurses in Korea and their knowledge structure for decision making. The differences of decision making models by nurse's knowledge structure were also tested. Research concepts were measured using the instrument based on systemic and interpretive decision making approaches that were developed by Lauri & Salantera (1995). The results were as follows. 1. The public health nurses turned to, most commonly, a mixed practical-theoretical knowledge structure (45.9%), followed by practical knowledge (32%) and theoretical knowledge (22.1%). 2. The six different decision making models were identified. These were named for decision making theories and nursing process. These were client-oriented decision making, rule-oriented systemic decision making, wholistic and intuitive decision making, decision making depending on subjective view and experience, systemic decision making for defining problems. 3. The public nurses who had practical and practical-theoretical knowledge structure and community health practitioner (CHP) retold that decision making depends on subjective view and experience. Also the public health nurses who had 5~19 years clinical experience represented hypothetico-deductive decision making for defining problems.

• 한국데이터정보과학회:학술대회논문집
• /
• 2005.04a
• /
• pp.164-173
• /
• 2005

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, data reduction and variable screening, category merging, etc. We analyze waste database united with local information using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.17 no.4
• /
• pp.101-111
• /
• 2014

Spatial events are predictable using data mining classification algorithms. Decision trees have been used as one of representative classification algorithms. And they were normally used in the classification tasks that have label class values. However since using rule ranking methods, spatial prediction have been applied in the spatial prediction problems. This paper compared rule ranking methods for the spatial prediction application using a decision tree. For the comparison experiment, C4.5 decision tree algorithm, and rule ranking methods such as Laplace, M-estimate and m-branch were implemented. As a spatial prediction case study, landslide which is one of representative spatial event occurs in the natural environment was applied. Among the rule ranking methods, in the results of accuracy evaluation, m-branch showed the better accuracy than other methods. However in case of m-brach and M-estimate required additional time-consuming procedure for searching optimal parameter values. Thus according to the application areas, the methods can be selectively used. The spatial prediction using a decision tree can be used not only for spatial predictions, but also for causal analysis in the specific event occurrence location.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2001.01a
• /
• pp.128-132
• /
• 2001

In this paper, we evaluate and contrast four neural network rule extraction approaches for credit scoring. Experiments are carried our on three real life credit scoring data sets. Both the continuous and the discretised versions of all data sets are analysed The rule extraction algorithms, Neurolonear, Neurorule. Trepan and Nefclass, have different characteristics, with respect to their perception of the neural network and their way of representing the generated rules or knowledge. It is shown that Neurolinear, Neurorule and Trepan are able to extract very concise rule sets or trees with a high predictive accuracy when compared to classical decision tree(rule) induction algorithms like C4.5(rules). Especially Neurorule extracted easy to understand and powerful propositional if -then rules for all discretised data sets. Hence, the Neurorule algorithm may offer a viable alternative for rule generation and knowledge discovery in the domain of credit scoring.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.6
• /
• pp.135-142
• /
• 2009

Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. The major techniques used in data mining are mining association rules, classification and clustering. Since these techniques are used individually, it is necessary to develop the methodology for rule extraction using a process of integrating these techniques. Rule extraction techniques assist humans in analyzing of large data sets and to turn the meaningful information contained in the data sets into successful decision making. This paper proposes an autonomous method of rule extraction using clustering and rough set theory. The experiments are carried out on data sets of UCI KDD archive and present decision rules from the proposed method. These rules can be successfully used for making decisions.

• The Journal of the Korea Contents Association
• /
• v.9 no.6
• /
• pp.336-343
• /
• 2009

Companies often use database for performing task more efficiently and data mining for marketing and production efficiency through analyzing of the stored database. The use of the knowledge through the data mining maintains and provides a direction of development for the company. It could be as an additional competitive power for the company when decision making is necessary. This study is designing a model that predicts a rating of existing customer and consumption pattern with using actual data of the manufacturer and data mining methodology. The objective of this model is to improve profits for the company and brand value through connecting the marketing with identifying the customer's rating and consumer behavior.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2023.05a
• /
• pp.389-390
• /
• 2023

To improve the cost performance of construction sites, various systems and standards are constantly being developed and implemented. Although legal requirements for these system and standard improvements have been increasing, the cost efficiency performance of construction sites remains stagnant. We have digitized documents generated through construction supervision work at 39 building construction sites and proposed a model that can support decision-making in cost efficiency evaluation. This model selects key keywords that are considered to be highly related to cost efficiency by identifying the patterns and relationships of keywords through associated rule analysis and social network analysis using keywords derived from documents. In addition, it is expected to be used as a decision-making aid to determine the cost efficiency of a specific building construction site by establishing a logistic regression model using core keywords. As a systematic database of construction supervision documents and an integrated system of massive data generated by digital technology are established in the future, the accuracy and reliability of the cost efficiency evaluation model are expected to be reinforced.

• Journal of Digital Convergence
• /
• v.11 no.5
• /
• pp.429-436
• /
• 2013

Context aware environment u-health service is to provide health service with recognition of a computer. The computer recognizes that a patient can contact real life in many context. Context aware environment service for recommend have to definition of context data and service recommendations related to factors shall be identified. In this paper, Context aware environment of u-health service will be provide context data related to identifies recommendations factors using multivariate analysis method and recommendations factors creation to decision tree, association rule based decision model. health service recommend for significantly context data can be distinguish through recommendation factors of identify. Also, context data of patient can know preference factors through preference decision model.

• Journal of KIISE:Information Networking
• /
• v.35 no.6
• /
• pp.497-509
• /
• 2008

Various algorithms and architectures for efficient packet classification have been widely studied. Packet classification algorithms based on a decision tree structure such as HiCuts and HyperCuts are known to be the best by exploiting the geometrical representation of rules in a classifier. However, the algorithms are not practical since they involve complicated heuristics in selecting a dimension of cuts and determining the number of cuts at each node of the decision tree. Moreover, the cutting is not efficient enough since the cutting is based on regular interval which is not related to the actual range that each rule covers. In this paper, we proposed a new efficient packet classification algorithm using a range cutting. The proposed algorithm primarily finds out the ranges that each rule covers in 2-dimensional prefix plane and performs cutting according to the ranges. Hence, the proposed algorithm constructs a very efficient decision tree. The cutting applied to each node of the decision tree is optimal and deterministic not involving the complicated heuristics. Simulation results for rule sets generated using class-bench databases show that the proposed algorithm has better performance in average search speed and consumes up to 3-300 times less memory space compared with previous cutting algorithms.

• Proceedings of the KIEE Conference
• /
• 2008.11a
• /
• pp.77-79
• /
• 2008

The power expansion planning is large and capital intensive capacity planning. In the past, the expansion planning was established with the proper supply reliability in order to minimize social cost. However, the planning not used cost minimizing objective function in the power markets with many market participants. This paper proposed the power expansion planning model using multi-criteria decision rule. This model used multi objective function considering not only cost minimizing but also GENCO's intension. This paper compared proposed model with WASP model in order to verify the result of proposed model.