Name
Abstract | ||
|---|---|---|
Time-series and sequences are important patterns in data mining. Based on an ontology of time-elements, this paper presents a formal characterization of time-series and state-sequences, where a state denotes a collection of data whose validation is dependent on time. While a time-series is formalized as a vector of time-elements temporally ordered one after another, a state-sequence is denoted as a list of states correspondingly ordered by a time-series. In general, a time-series and a state-sequence can be incomplete in various ways. This leads to the distinction between complete and incomplete time-series, and between complete and incomplete state-sequences, which allows the expression of both absolute and relative temporal knowledge in data mining. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/FSKD.2008.2 | FSKD (5) |
Keywords | Field | DocType |
artificial intelligence,data mining,data models,time series,ontologies,time series analysis,cognition | Ontology (information science),Time series,Data mining,Ontology,Data modeling,Information retrieval,Computer science,Cognition | Conference |
Volume | Issue | Citations |
5 | null | 4 |
PageRank | References | Authors |
0.43 | 19 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ma Jixin | 1 | 206 | 32.69 |
| Rongfang Bie | 2 | 547 | 68.23 |
| Guoxing Zhao | 3 | 16 | 3.43 |