Title :
Mining Negative Sequential Patterns in Transaction Databases
Author :
Ouyang, Wei-min ; Huang, Qin-hua
Author_Institution :
Shanghai Univ. of Sport, Shanghai
Abstract :
Sequential pattern is an important research topic in data mining and knowledge discovery. Sequential pattern is traditionally formed as (A, B) where A and B are frequent sequence in a transaction database. We extend this definition to include sequential patterns of forms (A, notB), (notA, B) and (notA, notB), which present negative sequential patterns among sequences. We call patterns of the form (A, B) positive sequential patterns, and patterns of the other forms negative patterns. Negative sequential patterns can also provide very useful insight view into the data set although they are different from positive ones. We put forward a discovery algorithm for mining negative sequential patterns from large transaction database in this paper.
Keywords :
data mining; data mining; frequent sequence; negative sequential patterns; transaction databases; Association rules; Conference management; Cybernetics; Data engineering; Data mining; Engineering management; Itemsets; Knowledge management; Machine learning; Transaction databases; Frequent sequence; Infrequent sequence; Negative sequential patterns; Sequential patterns;
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
DOI :
10.1109/ICMLC.2007.4370257