Name
Abstract | ||
|---|---|---|
Frequent Pattern Mining (FPM) is a very powerful paradigm which encompasses an entire class of data mining tasks. The specific tasks encompassed by FPM include the mining of increasingly complex and informative patterns, in complex structured and unstructured relational datasets, such as: Itemsets or co-occurrences [1] (transactional, unordered data), Sequences [2,8] (temporal or positional data, as in text mining, bioinformatics), Tree patterns [9] (XML/semistructured data), and Graph patterns [4,5,6] (complex relational data, bioinformatics). Figure [1] shows examples of these different types of patterns; in a generic sense a pattern denotes links/relationships between several objects of interest. The objects are denoted as nodes, and the links as edges. Patterns can have multiple labels, denoting various attributes, on both the nodes and edges. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11590316_12 | ICFCA |
Keywords | DocType | Volume |
unstructured relational datasets,massive datasets,complex relational data,generic container,towards generic pattern mining,particular pattern type,data mining task,different type,unordered data,complex datasets,common fpm task,positional data,text mining,database sizes increase,graph pattern,database management class,frequent pattern mining,data mining template library,semistructured data,graph mining | Conference | 3776 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-30506-8 | 14 |
PageRank | References | Authors |
0.79 | 26 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammed Javeed Zaki | 1 | 7972 | 536.24 |
| Nilanjana De | 2 | 15 | 1.16 |
| Feng Gao | 3 | 31 | 2.39 |
| Nagender Parimi | 4 | 15 | 1.16 |
| Benjarath Phoophakdee | 5 | 62 | 3.17 |
| Joe Urban Vineet Chaoji | 6 | 14 | 0.79 |
| Mohammad Al Hasan | 7 | 427 | 35.08 |
| Saeed Salem | 8 | 182 | 17.39 |