Knowledge discovery based on computational taxonomy and intelligent data mining

This study investigates an approach of knowledge discovery and data mining in insufficient databases. An application of Computational Taxonomy analysis demonstrates that the approach is effective in such a data mining process. The approach is characterized by the use of both the second type of domain knowledge and visualization. This type of knowledge is newly defined in this study and deduced from supposition about background situations of the domain. The supposition is triggered by strong intuition about the extracted features in a recurrent process of data mining. This type of domain knowledge is useful not only for discovering interesting knowledge but also for guiding the subsequent search for more explicit and interesting knowledge. The visualization is very useful for triggering the supposition.Área: Ingeniería de Software - Bases de DatosRed de Universidades con Carreras en Informática (RedUNCI

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining

Visually Mining Interesting Patterns in Multivariate Datasets

Data mining for patterns and knowledge discovery in multivariate datasets are very important processes and tasks to help analysts understand the dataset, describe the dataset, and predict unknown data values. However, conventional computer-supported data mining approaches often limit the user from getting involved in the mining process and performing interactions during the pattern discovery. Besides, without the visual representation of the extracted knowledge, the analysts can have difficulty explaining and understanding the patterns. Therefore, instead of directly applying automatic data mining techniques, it is necessary to develop appropriate techniques and visualization systems that allow users to interactively perform knowledge discovery, visually examine the patterns, adjust the parameters, and discover more interesting patterns based on their requirements. In the dissertation, I will discuss different proposed visualization systems to assist analysts in mining patterns and discovering knowledge in multivariate datasets, including the design, implementation, and the evaluation. Three types of different patterns are proposed and discussed, including trends, clusters of subgroups, and local patterns. For trend discovery, the parameter space is visualized to allow the user to visually examine the space and find where good linear patterns exist. For cluster discovery, the user is able to interactively set the query range on a target attribute, and retrieve all the sub-regions that satisfy the user\u27s requirements. The sub-regions that satisfy the same query and are neareach other are grouped and aggregated to form clusters. For local pattern discovery, the patterns for the local sub-region with a focal point and its neighbors are computationally extracted and visually represented. To discover interesting local neighbors, the extracted local patterns are integrated and visually shown to the analysts. Evaluations of the three visualization systems using formal user studies are also performed and discussed

Knowledge discovery based on computational taxonomy and intelligent data mining

This study investigates an approach of knowledge discovery and data mining in insufficient databases. An application of Computational Taxonomy analysis demonstrates that the approach is effective in such a data mining process. The approach is characterized by the use of both the second type of domain knowledge and visualization. This type of knowledge is newly defined in this study and deduced from supposition about background situations of the domain. The supposition is triggered by strong intuition about the extracted features in a recurrent process of data mining. This type of domain knowledge is useful not only for discovering interesting knowledge but also for guiding the subsequent search for more explicit and interesting knowledge. The visualization is very useful for triggering the supposition.Área: Ingeniería de Software - Bases de DatosRed de Universidades con Carreras en Informática (RedUNCI

A data mining approach to computational taxonomy

This study investigates an approach of knowledge discovery and data mining in insufficient databases. An application of Computational Taxonomy analysis demonstrates that the approach is effective in such a data mining process. The approach is characterized by the use ot both the second type of domain knowledge and visualization. This type of knowledge is newly defined in this study and deduced from supposition about background situations of the domain. The supposition is triggered by strong intuition about the extracted features in a recurrent process of data mining. This type of domain knowledge is useful not only for discovering interesting knowledge but al so tor guiding the subsequent search for more explicit and interesting knowledge. The visualization is very useful for triggering the supposition.Eje: Ingeniería de software y base de datosRed de Universidades con Carreras en Informática (RedUNCI