Name
Title | ||
|---|---|---|
Cluster based bit vector mining algorithm for finding frequent itemsets in temporal databases |
Abstract | ||
|---|---|---|
In this paper, we introduce an efficient algorithm using a new technique to find frequent itemsets from a huge set of itemsets called Cluster based Bit Vectors for Association Rule Mining (CBVAR). In this work, all the items in a transaction are converted into bits (0 or 1). A cluster is created by scanning the database only once. Then frequent 1-itemsets are extracted directly from the cluster table. Moreover, frequent k-itemsets, where k≥2 are obtained by using Logical AND between the items in a cluster table. This approach reduces main memory requirement since it considers only a small cluster at a time and as scalable for any large size of database. The overall performance of this method is significantly better than that of the previously developed algorithms for effective decision making. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.procs.2010.12.086 | Procedia Computer Science |
Keywords | Field | DocType |
Frequent itemsets,Association rule,Clustering,Cluster table,Bit vector,Temporal database | Data mining,Logical conjunction,Computer science,Association rule learning,Temporal database,Database transaction,Cluster analysis,Data mining algorithm,Bit array,Scalability | Journal |
Volume | ISSN | Citations |
3 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Krishnamurthy | 1 | 0 | 0.68 |
| A. Kannan | 2 | 195 | 25.98 |
| R. Baskaran | 3 | 122 | 11.58 |
| M. Kavitha | 4 | 0 | 0.34 |