Online mining association rules in data streams is an important field in the data
mining. Among them, mining the maximal frequent itemsets is also an important
issue. A frequent itemset is called maximal if it is not a subset of any other frequent
itemset. The set of all the maximal frequent itemsets is denoted as the maximal
frequent itemset. Because data streams are continuous, high speed, unbounded, and
real time. As a result, we can only scan once for the data streams. Therefore, the
previous algorithms to mine the maximal frequent itemsets in the traditional
databases are not suitable for the data streams. Furthermore, many applications are
interested in the recent data streams, and the sliding window is the model which
deal with the most recent data streams. In the sliding window model, a window size
is required. One of the algorithms for mining the maximal frequent itemsets based
on the sliding window model is called the MFIoSSW algorithm. The MFIoSSW
algorithm uses a compact structure to mine the maximal frequent itemsets. It uses
an array-based structure A to store the maximal frequent itemsets and other helpful
itemsets. But it takes long time to mine the maximal frequent itemsets. When the
new transaction comes, the number of comparison between the new transaction and
the old transactions is too much. Therefore, in this project, we propose a sliding
window approach, the Subset-Lattice algorithm. We use the lattice structure to store
the information of the transactions. The structure of the lattice stores the relationship
between the child node and the father node. In each node, we record the itemset and
the support. When the new transaction comes, we consider five relations: (1)
equivalent, (2) subset, (3) intersection, (4) empty set, (5) superset. With this five
relations, we can add the new transactions and update the support efficiently.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0709112-163748 |
| Date | 09 July 2012 |
| Creators | Wang, Syuan-Yun |
| Contributors | Ye-In Chang, Tei-Wei Kuo, Gen-Huey Chen, Chien-I Lee |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0709112-163748 |
| Rights | user_define, Copyright information available at source archive |
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