55,606 research outputs found
Mining Target-Oriented Sequential Patterns with Time-Intervals
A target-oriented sequential pattern is a sequential pattern with a concerned
itemset in the end of pattern. A time-interval sequential pattern is a
sequential pattern with time-intervals between every pair of successive
itemsets. In this paper we present an algorithm to discover target-oriented
sequential pattern with time-intervals. To this end, the original sequences are
reversed so that the last itemsets can be arranged in front of the sequences.
The contrasts between reversed sequences and the concerned itemset are then
used to exclude the irrelevant sequences. Clustering analysis is used with
typical sequential pattern mining algorithm to extract the sequential patterns
with time-intervals between successive itemsets. Finally, the discovered
time-interval sequential patterns are reversed again to the original order for
searching the target patterns.Comment: 11 pages, 9 table
Discovering Exclusive Patterns in Frequent Sequences
This paper presents a new concept for pattern discovery in frequent sequences with potentially interesting applications. Based on data mining, the approach aims to discover exclusive sequential patterns (ESP) by checking the relative exclusion of patterns across data sequences. ESP mining pursues the post-processing of sequential patterns and augments existing work on structural relations patterns mining. A three phase ESP mining method is proposed together with component algorithms, where a running worked example explains the process. Experiments are performed on real-world and synthetic datasets which showcase the results of ESP mining and demonstrate its effectiveness, illuminating the theories developed. An outline case study in workflow modelling gives some insight into future applicability
Coming: a Tool for Mining Change Pattern Instances from Git Commits
Software repositories such as Git have become a relevant source of
information for software engineer researcher. For instance, the detection of
Commits that fulfill a given criterion (e.g., bugfixing commits) is one of the
most frequent tasks done to understand the software evolution. However, to our
knowledge, there is not open-source tools that, given a Git repository, returns
all the instances of a given change pattern. In this paper we present Coming, a
tool that takes an input a Git repository and mines instances of change
patterns on each commit. For that, Coming computes fine-grained changes between
two consecutive revisions, analyzes those changes to detect if they correspond
to an instance of a change pattern (specified by the user using XML), and
finally, after analyzing all the commits, it presents a) the frequency of code
changes and b) the instances found on each commit. We evaluate Coming on a set
of 28 pairs of revisions from Defects4J, finding instances of change patterns
that involve If conditions on 26 of them
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