2 research outputs found
Utility Mining Across Multi-Dimensional Sequences
Knowledge extraction from database is the fundamental task in database and
data mining community, which has been applied to a wide range of real-world
applications and situations. Different from the support-based mining models,
the utility-oriented mining framework integrates the utility theory to provide
more informative and useful patterns. Time-dependent sequence data is commonly
seen in real life. Sequence data has been widely utilized in many applications,
such as analyzing sequential user behavior on the Web, influence maximization,
route planning, and targeted marketing. Unfortunately, all the existing
algorithms lose sight of the fact that the processed data not only contain rich
features (e.g., occur quantity, risk, profit, etc.), but also may be associated
with multi-dimensional auxiliary information, e.g., transaction sequence can be
associated with purchaser profile information. In this paper, we first
formulate the problem of utility mining across multi-dimensional sequences, and
propose a novel framework named MDUS to extract Multi-Dimensional
Utility-oriented Sequential useful patterns. Two algorithms respectively named
MDUS_EM and MDUS_SD are presented to address the formulated problem. The former
algorithm is based on database transformation, and the later one performs
pattern joins and a searching method to identify desired patterns across
multi-dimensional sequences. Extensive experiments are carried on five
real-life datasets and one synthetic dataset to show that the proposed
algorithms can effectively and efficiently discover the useful knowledge from
multi-dimensional sequential databases. Moreover, the MDUS framework can
provide better insight, and it is more adaptable to real-life situations than
the current existing models.Comment: Under review in IEEE TKDE, 14 page
Utility Mining Across Multi-Sequences with Individualized Thresholds
Utility-oriented pattern mining has become an emerging topic since it can
reveal high-utility patterns (e.g., itemsets, rules, sequences) from different
types of data, which provides more information than the traditional
frequent/confident-based pattern mining models. The utilities of various items
are not exactly equal in realistic situations; each item has its own utility or
importance. In general, user considers a uniform minimum utility (minutil)
threshold to identify the set of high-utility sequential patterns (HUSPs). This
is unable to find the interesting patterns while the minutil is set extremely
high or low. We first design a new utility mining framework namely USPT for
mining high-Utility Sequential Patterns across multi-sequences with
individualized Thresholds. Each item in the designed framework has its own
specified minimum utility threshold. Based on the lexicographic-sequential tree
and the utility-array structure, the USPT framework is presented to efficiently
discover the HUSPs. With the upper-bounds on utility, several pruning
strategies are developed to prune the unpromising candidates early in the
search space. Several experiments are conducted on both real-life and synthetic
datasets to show the performance of the designed USPT algorithm, and the
results showed that USPT could achieve good effectiveness and efficiency for
mining HUSPs with individualized minimum utility thresholds.Comment: Accepted by ACM Trans. on Data Science, 29 pages, 6 figure