To which extent can graph pattern mining enrich business intelligence? This question was the seed whose sprout became my PhD research. To find an answer, I investigated graph-based data integration, the calculation of business measures from graphs and suitable data mining techniques based thereon. The latter should identify correlations between occurrences of specific graph patterns and values ...

In this paper, we propose an approach to explore large texts by highlighting coherent sub-parts. The exploration method relies on a graph representation of the text according to Hoey’s linguistic model which allows the selection and the binding of adjacent and non-adjacent sentences. The main contribution of our work consists in proposing a method based on both Hoey’s linguistic model and a spe...

Detecting distributed anomalies rapidly and accurately is critical for efficient backbone network management. In this letter, we propose a novel anomaly detection method that uses router connection relationships to detect distributed anomalies in the backbone Internet. The proposed method unveils the underlying relationships among abnormal traffic behavior through closed frequent graph mining, ...

Opinion Mining aims at recognizing and categorizing or extracting opinions found in unstructured text resources and is one of the most dynamically evolving subdiscipline of Computational Linguistics showing some resemblance to document classification and information extraction tasks. In this paper we propose a novel approach in Opinion Mining which combines Machine Learning models based on trad...

Bipartite graphs have been proven useful in modeling a wide range of relationship networks. Finding the maximum edge biclique within a bipartite graph is a well-known problem in graph theory and data mining, with numerous realworld applications across different domains. We propose a probabilistic algorithm for finding the maximum edge biclique using a Monte Carlo subspace clustering approach. E...

Most text mining methods are based on representing documents using a vector space model, commonly known as a bag of word model, where each document is modeled as a linear vector representing the occurrence of independent words in the text corpus. It is well known that using this vector-based representation, important information, such as semantic relationship among concepts, is lost. This paper...

In this paper, we examine a new data mining issue of mining association rules from customer databases and transaction databases. The problem is decomposed into two subproblems: identifying all the large itemsets from the transaction database and mining association rules from the customer database and the large itemsets identified. For the first subproblem, we propose an efficient algorithm to d...

Finding recurring structural features among proteins three-dimensional (3D) structures is an important problem in bioinformatics. In this paper we apply a novel subgraph mining algorithm to three related graph representations of the sequence and proximity characteristics of a protein’s amino acid residues. The subgraph mining algorithm is used to discover spatial motifs that can be used to disc...