Add to ORKGAbstract. Due to the wide availability of huge data collection comprising multiple sequences that evolve over time, the process of adapting the classical data-mining techniques, making them capable to work into this new context, becomes today a strong necessity. In [1] we proposed a methodology permitting the application of a classification tree on sequential raw data and the extraction of the rules having a temporal dimension. In this article, we propose a formalism based on temporal first logic-order and we review the main steps of the methodology through this theoretical frame. Finally, we present some solutions for a practical implementation. 1
This thesis deals with a problem of mining sequential patterns from temporal databases. Data Mining...
This thesis deals with a problem of mining sequential patterns from temporal databases. Data Mining...
Data mining is concerned with analysing large volumes of (often unstructured) data to automatically ...
Summary. In this article we define a formalism for a methodology that has as pur-pose the discovery ...
The theoretical framework we proposed, based on first-order temporal logic, permits to define the ma...
A large volume of research in temporal data mining is focusing on discovering temporal rules from t...
A large volume of research in temporal data mining is focusing on discovering temporal rules from t...
A large volume of research in temporal data mining is focusing on discovering temporal rules from ti...
Abstract—The recent years and especially the Internet have changed the ways in which data is stored....
Extracting rules from temporal series is a well-established temporal data mining technique. The curr...
Supervised classification is one of the main computational tasks of modern Artificial Intelligence, ...
Session S: Time Series, String, Stream, Online LearningInternational audienceWe introduce a temporal...
Session S: Time Series, String, Stream, Online LearningInternational audienceWe introduce a temporal...
Abstract. Data mining is concerned with analysing large volumes of (often unstructured) data to auto...
Abstract. One of the main unresolved problems that arise during the data mining process is treating ...
This thesis deals with a problem of mining sequential patterns from temporal databases. Data Mining...
This thesis deals with a problem of mining sequential patterns from temporal databases. Data Mining...
Data mining is concerned with analysing large volumes of (often unstructured) data to automatically ...
Summary. In this article we define a formalism for a methodology that has as pur-pose the discovery ...
The theoretical framework we proposed, based on first-order temporal logic, permits to define the ma...
A large volume of research in temporal data mining is focusing on discovering temporal rules from t...
A large volume of research in temporal data mining is focusing on discovering temporal rules from t...
A large volume of research in temporal data mining is focusing on discovering temporal rules from ti...
Abstract—The recent years and especially the Internet have changed the ways in which data is stored....
Extracting rules from temporal series is a well-established temporal data mining technique. The curr...
Supervised classification is one of the main computational tasks of modern Artificial Intelligence, ...
Session S: Time Series, String, Stream, Online LearningInternational audienceWe introduce a temporal...
Session S: Time Series, String, Stream, Online LearningInternational audienceWe introduce a temporal...
Abstract. Data mining is concerned with analysing large volumes of (often unstructured) data to auto...
Abstract. One of the main unresolved problems that arise during the data mining process is treating ...
This thesis deals with a problem of mining sequential patterns from temporal databases. Data Mining...
This thesis deals with a problem of mining sequential patterns from temporal databases. Data Mining...
Data mining is concerned with analysing large volumes of (often unstructured) data to automatically ...