17,951 research outputs found
Sequential Patterns Post-processing for Structural Relation Patterns Mining
Sequential patterns mining is an important data-mining technique used to identify frequently observed sequential
occurrence of items across ordered transactions over time. It has been extensively studied in the literature, and there
exists a diversity of algorithms. However, more complex structural patterns are often hidden behind sequences.
This article begins with the introduction of a model for the representation of sequential patternsâSequential
Patterns Graphâwhich motivates the search for new structural relation patterns. An integrative framework for
the discovery of these patternsâPostsequential Patterns Miningâis then described which underpins the postprocessing
of sequential patterns. A corresponding data-mining method based on sequential patterns postprocessing
is proposed and shown to be effective in the search for concurrent patterns. From experiments conducted on three
component algorithms, it is demonstrated that sequential patterns-based concurrent patterns mining provides
an efficient method for structural knowledge discover
On mining complex sequential data by means of FCA and pattern structures
Nowadays data sets are available in very complex and heterogeneous ways.
Mining of such data collections is essential to support many real-world
applications ranging from healthcare to marketing. In this work, we focus on
the analysis of "complex" sequential data by means of interesting sequential
patterns. We approach the problem using the elegant mathematical framework of
Formal Concept Analysis (FCA) and its extension based on "pattern structures".
Pattern structures are used for mining complex data (such as sequences or
graphs) and are based on a subsumption operation, which in our case is defined
with respect to the partial order on sequences. We show how pattern structures
along with projections (i.e., a data reduction of sequential structures), are
able to enumerate more meaningful patterns and increase the computing
efficiency of the approach. Finally, we show the applicability of the presented
method for discovering and analyzing interesting patient patterns from a French
healthcare data set on cancer. The quantitative and qualitative results (with
annotations and analysis from a physician) are reported in this use case which
is the main motivation for this work.
Keywords: data mining; formal concept analysis; pattern structures;
projections; sequences; sequential data.Comment: An accepted publication in International Journal of General Systems.
The paper is created in the wake of the conference on Concept Lattice and
their Applications (CLA'2013). 27 pages, 9 figures, 3 table
XML Schema Clustering with Semantic and Hierarchical Similarity Measures
With the growing popularity of XML as the data representation language, collections of the XML data are exploded in numbers. The methods are required to manage and discover the useful information from them for the improved document handling. We present a schema clustering process by organising the heterogeneous XML schemas into various groups. The methodology considers not only the linguistic and the context of the elements but also the hierarchical structural similarity. We support our findings with experiments and analysis
Applications of concurrent access patterns in web usage mining
This paper builds on the original data mining and modelling research which has proposed the discovery of novel structural relation patterns, applying the approach in web usage mining. The focus of attention here is on concurrent access patterns (CAP), where an overarching framework illuminates the methodology for web access patterns post-processing. Data pre-processing, pattern discovery and patterns analysis all proceed in association with access patterns mining, CAP mining and CAP modelling. Pruning and selection of access pat-terns takes place as necessary, allowing further CAP mining and modelling to be pursued in the search for the most interesting concurrent access patterns. It is shown that higher level CAPs can be modelled in a way which brings greater structure to bear on the process of knowledge discovery. Experiments with real-world datasets highlight the applicability of the approach in web navigation
Problem-Solving Knowledge Mining from Usersâ\ud Actions in an Intelligent Tutoring System
In an intelligent tutoring system (ITS), the domain expert should provide\ud
relevant domain knowledge to the tutor so that it will be able to guide the\ud
learner during problem solving. However, in several domains, this knowledge is\ud
not predetermined and should be captured or learned from expert users as well as\ud
intermediate and novice users. Our hypothesis is that, knowledge discovery (KD)\ud
techniques can help to build this domain intelligence in ITS. This paper proposes\ud
a framework to capture problem-solving knowledge using a promising approach\ud
of data and knowledge discovery based on a combination of sequential pattern\ud
mining and association rules discovery techniques. The framework has been implemented\ud
and is used to discover new meta knowledge and rules in a given domain\ud
which then extend domain knowledge and serve as problem space allowing\ud
the intelligent tutoring system to guide learners in problem-solving situations.\ud
Preliminary experiments have been conducted using the framework as an alternative\ud
to a path-planning problem solver in CanadarmTutor
A log mining approach for process monitoring in SCADA
SCADA (Supervisory Control and Data Acquisition) systems are used for controlling and monitoring industrial processes. We propose a methodology to systematically identify potential process-related threats in SCADA. Process-related threats take place when an attacker gains user access rights and performs actions, which look legitimate, but which are intended to disrupt the SCADA process. To detect such threats, we propose a semi-automated approach of log processing. We conduct experiments on a real-life water treatment facility. A preliminary case study suggests that our approach is effective in detecting anomalous events that might alter the regular process workflow
Exploring the Evolution of Node Neighborhoods in Dynamic Networks
Dynamic Networks are a popular way of modeling and studying the behavior of
evolving systems. However, their analysis constitutes a relatively recent
subfield of Network Science, and the number of available tools is consequently
much smaller than for static networks. In this work, we propose a method
specifically designed to take advantage of the longitudinal nature of dynamic
networks. It characterizes each individual node by studying the evolution of
its direct neighborhood, based on the assumption that the way this neighborhood
changes reflects the role and position of the node in the whole network. For
this purpose, we define the concept of \textit{neighborhood event}, which
corresponds to the various transformations such groups of nodes can undergo,
and describe an algorithm for detecting such events. We demonstrate the
interest of our method on three real-world networks: DBLP, LastFM and Enron. We
apply frequent pattern mining to extract meaningful information from temporal
sequences of neighborhood events. This results in the identification of
behavioral trends emerging in the whole network, as well as the individual
characterization of specific nodes. We also perform a cluster analysis, which
reveals that, in all three networks, one can distinguish two types of nodes
exhibiting different behaviors: a very small group of active nodes, whose
neighborhood undergo diverse and frequent events, and a very large group of
stable nodes
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