Discovering unexpected and useful patterns in databases is a fundamental data mining task.
In recent years, a trend in data mining has been to design algorithms for discovering …

Recently, the advancement of cognitive computing and three-way decisions has enabled in-
depth sequential pattern understanding through temporal association analysis. The main …

Programmers often consult an online Q&A forum such as Stack Overflow to learn new APIs.
This paper presents an empirical study on the prevalence and severity of API misuse on …

GPS technology is widely used to produce detailed data on the movement of people.
Analysing massive amounts of GPS data, however, can be cumbersome. We present a …

In recent years, software defects have become the dominant cause of unplanned outage;
improvements in software reliability and quality have not kept pace with those of hardware …

Sequential pattern mining (SPM) has been applied in many fields. However, traditional SPM
neglects the pattern repetition in sequence. To solve this problem, gap constraint SPM was …

Event sequences analysis plays an important role in many application domains such as
customer behavior analysis, electronic health record analysis and vehicle fault diagnosis …

In this paper, we propose a new algorithm, called ClaSP for mining frequent closed
sequential patterns in temporal transaction data. Our algorithm uses several efficient search …

OBJECTIVE: Clinical pathway analysis, as a pivotal issue in ensuring specialized,
standardized, normalized and sophisticated therapy procedures, is receiving increasing …

Mining sequential patterns is an important research issue in data mining and knowledge
discovery with broad applications. However, the existing sequential pattern mining …