Name
Abstract | ||
|---|---|---|
Mining frequent sequences is a step in the sequential patterns discovering, and sequential patterns mining is an important area of research in the field of data mining. If we use the traditional algorithms such as Apriori or GSP algorithm to discover the sequential patterns under the circumstance of the dynamic data changing, since they need to scan the whole database for multiple times, and do not give the right information at the right time, so the results don't reflect the current status, and the performances will become inefficient. In this paper, we present a new method for mining the frequent sequences in dynamic environment,. the method is developed based on previous episodes mining results. It only needs to scan parts of the whole dataset based on the previous results for the whole frequent sequences mining at the end, and it only needs to scan the database only once in the special situation. Experimental results show that the performance of our algorithm outperforms the GPS algorithm very greatly. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/GRC.2009.5255101 | 2009 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING ( GRC 2009) |
Keywords | Field | DocType |
sequence mining,probability density function,sequential pattern mining,algorithm design and analysis,data mining,dynamic data | Data mining,Algorithm design,GSP Algorithm,Computer science,A priori and a posteriori,Algorithm,FSA-Red Algorithm,Dynamic data,Global Positioning System,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guangyuan Li | 1 | 23 | 11.34 |
| Xiao Qin | 2 | 1 | 3.40 |
| Qinbin Hu | 3 | 0 | 0.34 |
| Chang-an Yuan | 4 | 85 | 9.88 |