the issue, including one by Annalisa Appice, Claudia D'Amato, Floriana Esposito and Donato Malerba which presents a lazy-learning approach for symbolic objects and another by Yannis Tzitzikas which proposes a technique for compressing a symbolic data table using the compound term composition algebra...
Spatiotemporal dataAttributed DAGWeighted pathEnvironmental monitoringDirected acyclic graphs (DAGs) are used in many domains ranging from computer science to bioinformatics, including industry and geoscience. They enable to model complex evolutions where spatial objects (e.g., soil erosion) may move, (...
Mining Complex Spatio-Temporal Sequence Patterns Download Preview More and more sources of streaming spatio-temporal data. ► tra c sensors, GPS, mobile devices, RFID, etc. ► Often, data describes the movement of objects between Satelite tracking of Caribou (noisy, sparse and missing data...
This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey...
Handling of relational and complex types of data:Because relational databases and data warehouses are widely used, the development of efficient and effective data mining systems for such data is important. However, other databases may contain complex data objects, hypertext and multimedia data, spatial...
Data Filter Function Incremental Mining based on Feature Selection in an Active Distribution Network Compared with traditional distributed networks, the complex access environment, flexible access mode, massive access terminal, and data in an active distri... S Deng,Q Cai,Z Zhang,... - IET Cyber...
(NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not ...
Data mining is the process of discovering actionable information from large sets of data. Data mining uses mathematical analysis to derive patterns and trends that exist in data. Typically, these patterns cannot be discovered by traditional data exploration because the relationships are too complex or...
Mining Complex Spatio-Temporal Sequence Patterns Mining sequential movement patterns describing group behaviour in potentially streaming spatio-temporal data sets is a challenging problem. Movements are typically noisy and often overlap each other. This makes a set of simple patterns d... F Verhein -...
Integrated data are then elaborated to compute interesting key performing indicators (KPI) and/or mined with complex data mining algorithms to discover previous unknown and interesting knowledge. The general architecture of this kind of system is shown in Fig. 4. Four different layers can be ...