| dc.creator (作者) | C-W- Cho;Y-H- Wu;Chen, Arbee L. P. | en_US |
| dc.creator (作者) | 陳良弼 | - |
| dc.date (日期) | 2009-02 | en_US |
| dc.date.accessioned | 16-十二月-2008 16:43:39 (UTC+8) | - |
| dc.date.available | 16-十二月-2008 16:43:39 (UTC+8) | - |
| dc.date.issued (上傳時間) | 16-十二月-2008 16:43:39 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/14998 | - |
| dc.description.abstract (摘要) | In this paper, we propose a novel algorithm for mining frequent sequences from transaction databases. The transactions of the same customers form a set of customer sequences. A sequence (an ordered list of itemsets) is frequent if the number of customer sequences containing it satisfies the user-specified threshold. The 1-sequence is a special type of sequences because it consists of only a single itemset instead of an ordered list, while the k-sequence is a sequence composed of k itemsets. Compared with the cost of mining frequent k-sequences (k ≥ 2), the cost of mining frequent 1-sequences is negligible. We adopt a two-phase architecture to find the two types of frequent sequences separately in order that the discovery of frequent k-sequences can be well designed and optimized. For efficient frequent k-sequence mining, every frequent 1-sequence is encoded as a unique symbol and the database is transformed into one constituted by the symbols. We find that it is unnecessary to encode all the frequent 1-seqences, and make full use of the discovered frequent 1-sequences to transform the database into one with a smaller size. For every k ≥ 2, the customer sequences in the transformed database are scanned to find all the frequent k-sequences. We devise the compact representation for a customer sequence and elaborate the method to enumerate all distinct subsequences from a customer sequence without redundant scans. The soundness of the proposed approach is verified and a number of experiments are performed. The results show that our approach outperforms the previous works in both scalability and execution time. | en_US |
| dc.format | application/ | en_US |
| dc.language | en | en_US |
| dc.language | en-US | en_US |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Journal of Intelligent Information Systems, 32(1), 23-51 | en_US |
| dc.subject (關鍵詞) | Data mining;Sequential patterns;Database transformation;Support computation;Database projection | en_US |
| dc.title (題名) | Effective Database Transformation and Efficient Support Computation for Mining Sequential Patterns | en_US |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1007/s10844-007-0047-y | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1007/s10844-007-0047-y | en_US |
