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Frequent serial episodes within an event sequence describe the behavior of users or systems about the application. Existing mining algorithms calculate the frequency of an episode based on overlapping or non-minimal occurrences, which is prone to over-counting the support of long episodes or poorly characterizing the followed-by-closely relationship over(More)
Serial episode mining is one of hot spots in temporal data mining with broad applications such as user-browsing behavior prediction, telecommunication alarm analysis, road traffic monitoring, and root cause diagnostics from faults log data in manufacturing. In this paper, as a step forward to analyzing patterns within an event sequence, we propose a novel(More)
The discovery of causal relationships between observed variables has received much attention in the past. For continuous-valued data, linear acyclic causal models are commonly used to model the data-generating process, and structural equation models, bayesian networks are widely applied to analyze the structures. In reality, many causal relationships are(More)
Stream prediction based on episode rules of the form "whenever a series of antecedent event types occurs, another series of consequent event types appears eventually"has received intensive attention due to its broad applications such as reading sequence forecasting, stock trend analyzing, road traffic monitoring, and software fault preventing. Many previous(More)
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