Name
Title | ||
|---|---|---|
Context-Aware Data Mining vs Classical Data Mining - Case Study on Predicting Soil Moisture. |
Abstract | ||
|---|---|---|
The purpose of this article is to investigate whether or not including context data (context-awareness) in a classical data mining process would enhance the overall results. For that, the efficiency of the predictions was analyzed and compared: in a classical data mining process versus a context-aware data mining process. The two processes were applied on existing data collected from more weather stations to predict the future soil moisture. The classic data mining process considers historical data on soil moisture and temperature in a time interval, while the context-aware process also adds collected context information on air temperature for that location. The obtained results show advantages of CADM over classical DM. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-20055-8_19 | 14TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS (SOCO 2019) |
Keywords | Field | DocType |
Context-aware data mining,Classical data mining,Soil moisture predictions,Machine learning | Data mining,Computer science,Artificial intelligence,Air temperature,Water content,Machine learning | Conference |
Volume | ISSN | Citations |
950 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anca Avram | 1 | 1 | 1.03 |
| Oliviu Matei | 2 | 43 | 11.15 |
| Camelia-Mihaela Pintea | 3 | 102 | 16.15 |
| Petrica C. Pop | 4 | 183 | 27.86 |
| Carmen Ana Anton | 5 | 0 | 0.34 |