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Selected Sections of Information Management
Published in Paul Schönsleben, Integral Logistics Management, 2018
To each object or each entity, a number of attributes belong that contain all information about the object or entity that is of interest in the context studied. Each attribute describes the entity or object in a certain way; it shows one aspect of it. For each attribute, there is a corresponding attribute domain; different attributes can have the same attribute domain.
Change-Detection Machine Learning Model for Educational Management
Published in Cybernetics and Systems, 2023
Originally, the attributes of data (i.e. items) handled with association rules mining are nominal. Data with quantitative attributes are not processed in traditional association rule mining. Therefore, Srikant and Agrawal (1996) proposed an approach to handle quantitative data and rules discovered from them called quantitative association rules. Their approach partitions the attribute domain of quantitative data and combines adjacent partitions, causing a sharp boundary problem (Kuok, Fu, and Wong 1998). Hence, Kuok, Fu, and Wong (1998) used a fuzzy set concept to mine association rules in quantitative data. The rules they discovered are called fuzzy association rules (FAR). An example of this kind of rule is Attendance = Low∧Final Report = Middle→Semester = Middle. The quantitative data are employed with fuzzy sets and represented with linguistic terms. The discovered rules are more understandable to humans and are not sensitive to small changes of boundaries (Kuok, Fu, and Wong 1998; Delgado et al. 2003). Later, Yu, Own, and Lin (2001) applied fuzzy association rules to discover the relationships between each pattern of a learner’s activities in a web-based course. Delgado et al. (2003) proposed a general model for fuzzy association rules and discussed applications. Au and Chan (2002, 2005) employed linguistic variables and linguistic terms to represent changes in supports and confidences of association rules over different time-periods.
A hybrid approach of case-based reasoning and process reasoning to typical parts grinding process intelligent decision
Published in International Journal of Production Research, 2023
Zhongyang Li, Zhaohui Deng, Zhiguang Ge, Lishu Lv, Jimin Ge
For enumeration feature attribute, the determination of these feature attribute values strongly relies on the subjective knowledge of human beings, which are generally given based on experience, and among the attribute values have a certain relationship in the attribute domain. Hence, the local similarity calculation is based on the formula as followed: where M represents the highest value of the feature enumeration . The assignment values of surface burn are shown in Table 1. In addition, the local similarity of some feature attributes needs to be determined based on domain knowledge. For example, the local similarity of the material heat treatment method is shown in Table 2, and its value represents the difference in processing performance between the two material states.
Error by omitted variables in regret-based choice models: formal and empirical comparison with utility-based models using orthogonal design data
Published in Transportmetrica A: Transport Science, 2020
Sunghoon Jang, Soora Rasouli, Harry Timmermans
Rasouli and Timmermans (2017) theoretically and empirically compared these three types of regret, and argued that Rmax is theoretically more appealing and a more valid representation of the concept of regret. The model was shown to outperform the other two specifications in a dedicated example with small attribute differences, the attribute domain where one expects the largest differences between the model specifications. Additional data are however required to judge the generalizability of this finding. With larger attribute differences, the different specifications of the regret function will lead to asymptotically identical models.