Name
Abstract | ||
|---|---|---|
Topics in 0-1 datasets are sets of variables whose occurrences are positively connected together. Earlier, we described a simple generative topic model. In this paper we show that, given data produced by this model, the lift statistics of attributes can be described in matrix form. We use this result to obtain a simple algorithm for finding topics in 0-1 data. We also show that a problem related to the identification of topics is NP-hard. We give experimental results on the topic identification problem, both on generated and real data. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/978-3-540-39804-2_38 | Lecture Notes in Artificial Intelligence |
Field | DocType | Volume |
Data mining,Computer science,Latent variable model,Non-negative matrix factorization,Artificial intelligence,Binary data,SIMPLE algorithm,Topic model,System identification,Latent semantic analysis,Machine learning,Parameter identification problem | Conference | 2838 |
ISSN | Citations | PageRank |
0302-9743 | 12 | 1.30 |
References | Authors | |
21 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jouni K. Seppänen | 1 | 124 | 9.09 |
| Ella Bingham | 2 | 917 | 58.70 |
| Heikki Mannila | 3 | 6595 | 1495.69 |