Abstract
This paper describes the application of data mining methods in the database of the DORIS transportation information system, currently used by the Prague Public Transit Company. The goal is to create knowledge about the behavior of objects within this information system. Data is analyzed partly with the help of descriptive statistical methods, and partly with the help of association rules, which may discover common combinations of attributes that occur most frequently within a given data set. Two types of quantifiers were used when creating the association rules; namely “founded implication” and “above average”. The results of the analysis are presented in the form of graphs and hypotheses.
Highlights
Data mining can be defined as the non-trivial extraction of implicit, previously unknown, yet potentially useful information from data, and may be defined as the science of extracting useful information from large data sets or databases
One standard, named CRISP-DM (Cross-Industry Standard Process for Data Mining), describes this process step by step. It develops each phase of the knowledge discovery in databases (KDD) process and, in addition, helps to avoid common mistakes
The conclusions drawn from such analyses provide an exact tool for recognizing black spots, and suitable measures can be taken, e.g., decisions about tram preference at traffic lights or about constructing some suitable passive elements
Summary
Data mining can be defined as the non-trivial extraction of implicit, previously unknown, yet potentially useful information from data, and may be defined as the science of extracting useful information from large data sets or databases. With the help of data mining, derived knowledge, relationships and conclusions are often represented as models or patterns. Data mining only implies modeling or an analytical method in this application sense, and is considered to be a part of the KDD process [1]. One standard, named CRISP-DM (Cross-Industry Standard Process for Data Mining), describes this process step by step. It develops each phase of the KDD process and, in addition, helps to avoid common mistakes. We have to begin by uncovering important factors that can influence the outcome of the project This task involves more detailed fact-finding about all of the resources, assumptions and other factors that should be considered in determining the data analysis goal
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
