Acm Transactions on Knowledge Discovery from Data, 2012
Abstract Clustering data described by categorical attributes is a challenging task in data mining... more Abstract Clustering data described by categorical attributes is a challenging task in data mining applications. Unlike numerical attributes, it is difficult to define a distance between pairs of values of a categorical attribute, since the values are not ordered. In this article, we propose a framework to learn a context-based distance for categorical attributes. The key intuition of this work is that the distance between two values of a categorical attribute A i can be determined by the way in which the values of the other attributes A j are distributed in ...
Abstract The availability of data represented with multiple features coming from heterogeneous do... more Abstract The availability of data represented with multiple features coming from heterogeneous domains is getting more and more common in real world applications. Such data represent objects of a certain type, connected to other types of data, the features, so that the overall data schema forms a star structure of inter-relationships. Co-clustering these data involves the specification of many parameters, such as the number of clusters for the object dimension and for all the features domains. In this paper we present a novel co- ...
ABSTRACT One of the exciting scientific challenges in functional genomics concerns the discovery ... more ABSTRACT One of the exciting scientific challenges in functional genomics concerns the discovery of biologically relevant patterns from gene expression data. For instance, it is extremely useful to provide putative synexpression groups or transcription modules to molecular biologists. We propose a methodology that has been proved useful in real cases. It is described as a prototypical KDD scenario which starts from raw expression data selection until useful patterns are delivered. It has been validated on real data sets. Our conceptual contribution is (a) to emphasize how to take the most from recent progress in constraint-based mining of set patterns, and (b) to propose a generic approach for gene expression data enrichment. Doing so, we survey our algorithmic breakthrough which has been the core of our contribution to the IST FET cInQ project.
CiteSeerX - Document Details (Isaac Councill, Lee Giles): Abstract. Within 0/1 data, co-clusterin... more CiteSeerX - Document Details (Isaac Councill, Lee Giles): Abstract. Within 0/1 data, co-clustering provides a collection of biclusters, ie, linked clusters for both objects and Boolean properties. Beside the classical need for grouping quality optimization, one can also use user-defined ...
CiteSeerX - Document Details (Isaac Councill, Lee Giles): Abstract. Thanks to an important resear... more CiteSeerX - Document Details (Isaac Councill, Lee Giles): Abstract. Thanks to an important research effort the last few years, inductive queries on local patterns (eg, set patterns) and complete solvers which can evaluate them on large data sets have been proved extremely useful ...
Abstract. In the domain of gene expression data analysis, several re-searchers have recently emph... more Abstract. In the domain of gene expression data analysis, several re-searchers have recently emphasized the promising application of local pattern (eg, association rules, closed sets) discovery techniques from boolean matrices that encode gene properties. Detecting local patterns ...
IEEE Transactions on Neural Networks and Learning Systems, 2016
In this paper, we introduce a new approach of semisupervised anomaly detection that deals with ca... more In this paper, we introduce a new approach of semisupervised anomaly detection that deals with categorical data. Given a training set of instances (all belonging to the normal class), we analyze the relationship among features for the extraction of a discriminative characterization of the anomalous instances. Our key idea is to build a model that characterizes the features of the normal instances and then use a set of distance-based techniques for the discrimination between the normal and the anomalous instances. We compare our approach with the state-of-the-art methods for semisupervised anomaly detection. We empirically show that a specifically designed technique for the management of the categorical data outperforms the general-purpose approaches. We also show that, in contrast with other approaches that are opaque because their decision cannot be easily understood, our proposed approach produces a discriminative model that can be easily interpreted and used for the exploration of the data.
Résumé. Dans de nombreuses applications, une co-classification est plus facile à interpréter qu&#... more Résumé. Dans de nombreuses applications, une co-classification est plus facile à interpréter qu'une classification mono-dimensionnelle. Il s'agit de calculer une bi-partition ou collection de co-clusters : chaque co-cluster est un groupe d'ob-jets associé à un groupe d'attributs ...
* Laboratoire Hubert Curien (CNRS - Université de Saint-Etienne) 18 Rue Benoit Lauras F-42023 Sai... more * Laboratoire Hubert Curien (CNRS - Université de Saint-Etienne) 18 Rue Benoit Lauras F-42023 Saint-Etienne cedex 2 ruggero.pensa@univ-st-etienne.frbaptiste.jeudy@univ-st-etienne.fr ... RÉSUMÉ. A définir par la commande \resum e{ ...} ... ABSTRACT. A définir par la ...
Abstract. Clustering or bi-clustering techniques have been proved quite useful in many applicatio... more Abstract. Clustering or bi-clustering techniques have been proved quite useful in many application domains. A weakness of these techniques re-mains the poor support for grouping characterization. We consider even-tually large Boolean data sets which record ...
Intl. Workshop on Pattern Representation and Management, 2004
We are designing new data mining techniques on gene ex- pression data, more precisely inductive q... more We are designing new data mining techniques on gene ex- pression data, more precisely inductive querying techniques that extract a priori interesting bi-sets, i.e., sets of objects (or biological situations) and associated sets of attributes (or genes). The so-called (formal) con- cepts are important special cases of a priori interesting bi-sets in derived boolean expression matrices, e.g., matrices that encode
SIAM International Conference on Data Mining, 2008
In many applications, the expert interpretation of co- clustering is easier than for mono-dimensi... more In many applications, the expert interpretation of co- clustering is easier than for mono-dimensional clustering. Co-clustering aims at computing a bi-partition that is a col- lection of co-clusters: each co-cluster is a group of objects associated to a group of attributes and these associations can support interpretations. Many constrained clustering algo- rithms have been proposed to exploit the domain knowledge
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, 2008
ABSTRACT We investigate a co-clustering framework (i.e., a method that provides a partition of ob... more ABSTRACT We investigate a co-clustering framework (i.e., a method that provides a partition of objects and a linked partition of features) for binary data sets. So far, constrained co-clustering has been seldomly explored. First, we consider straightforward extensions of the classical instance level constraints (must-link, cannot-link) to express relationships on both objects and features. Furthermore, we study constraints that exploit sequential orders on objects and/or features. The idea is that we can specify whether the extracted co-chisters should involve or not contiguous elements (interval and non-interval constraints). Instead of designing constraint processing integration within a co-clustering scheme, we propose a local-to-global (L2G) framework. It consists of postprocessing a collection of (constrained) local patterns that have been computed beforehand (e.g., closed feature sets and their supporting sets of objects) to build a global pattern like a co-clustering. Roughly speaking, the algorithmic scheme is a k-means-like approach that groups the local patterns. We show that it is possible to push local counterparts of the global constraints on the co-clusters during the local pattern mining phase itself. A large part of the chapter is dedicated to experiments that demonstrate the added-value of our approach. Considering both synthetic data and real gene expression data sets, we discuss the use of constraints to get not only more stable but also more relevant co-clusters.
Acm Transactions on Knowledge Discovery from Data, 2012
Abstract Clustering data described by categorical attributes is a challenging task in data mining... more Abstract Clustering data described by categorical attributes is a challenging task in data mining applications. Unlike numerical attributes, it is difficult to define a distance between pairs of values of a categorical attribute, since the values are not ordered. In this article, we propose a framework to learn a context-based distance for categorical attributes. The key intuition of this work is that the distance between two values of a categorical attribute A i can be determined by the way in which the values of the other attributes A j are distributed in ...
Abstract The availability of data represented with multiple features coming from heterogeneous do... more Abstract The availability of data represented with multiple features coming from heterogeneous domains is getting more and more common in real world applications. Such data represent objects of a certain type, connected to other types of data, the features, so that the overall data schema forms a star structure of inter-relationships. Co-clustering these data involves the specification of many parameters, such as the number of clusters for the object dimension and for all the features domains. In this paper we present a novel co- ...
ABSTRACT One of the exciting scientific challenges in functional genomics concerns the discovery ... more ABSTRACT One of the exciting scientific challenges in functional genomics concerns the discovery of biologically relevant patterns from gene expression data. For instance, it is extremely useful to provide putative synexpression groups or transcription modules to molecular biologists. We propose a methodology that has been proved useful in real cases. It is described as a prototypical KDD scenario which starts from raw expression data selection until useful patterns are delivered. It has been validated on real data sets. Our conceptual contribution is (a) to emphasize how to take the most from recent progress in constraint-based mining of set patterns, and (b) to propose a generic approach for gene expression data enrichment. Doing so, we survey our algorithmic breakthrough which has been the core of our contribution to the IST FET cInQ project.
CiteSeerX - Document Details (Isaac Councill, Lee Giles): Abstract. Within 0/1 data, co-clusterin... more CiteSeerX - Document Details (Isaac Councill, Lee Giles): Abstract. Within 0/1 data, co-clustering provides a collection of biclusters, ie, linked clusters for both objects and Boolean properties. Beside the classical need for grouping quality optimization, one can also use user-defined ...
CiteSeerX - Document Details (Isaac Councill, Lee Giles): Abstract. Thanks to an important resear... more CiteSeerX - Document Details (Isaac Councill, Lee Giles): Abstract. Thanks to an important research effort the last few years, inductive queries on local patterns (eg, set patterns) and complete solvers which can evaluate them on large data sets have been proved extremely useful ...
Abstract. In the domain of gene expression data analysis, several re-searchers have recently emph... more Abstract. In the domain of gene expression data analysis, several re-searchers have recently emphasized the promising application of local pattern (eg, association rules, closed sets) discovery techniques from boolean matrices that encode gene properties. Detecting local patterns ...
IEEE Transactions on Neural Networks and Learning Systems, 2016
In this paper, we introduce a new approach of semisupervised anomaly detection that deals with ca... more In this paper, we introduce a new approach of semisupervised anomaly detection that deals with categorical data. Given a training set of instances (all belonging to the normal class), we analyze the relationship among features for the extraction of a discriminative characterization of the anomalous instances. Our key idea is to build a model that characterizes the features of the normal instances and then use a set of distance-based techniques for the discrimination between the normal and the anomalous instances. We compare our approach with the state-of-the-art methods for semisupervised anomaly detection. We empirically show that a specifically designed technique for the management of the categorical data outperforms the general-purpose approaches. We also show that, in contrast with other approaches that are opaque because their decision cannot be easily understood, our proposed approach produces a discriminative model that can be easily interpreted and used for the exploration of the data.
Résumé. Dans de nombreuses applications, une co-classification est plus facile à interpréter qu&#... more Résumé. Dans de nombreuses applications, une co-classification est plus facile à interpréter qu'une classification mono-dimensionnelle. Il s'agit de calculer une bi-partition ou collection de co-clusters : chaque co-cluster est un groupe d'ob-jets associé à un groupe d'attributs ...
* Laboratoire Hubert Curien (CNRS - Université de Saint-Etienne) 18 Rue Benoit Lauras F-42023 Sai... more * Laboratoire Hubert Curien (CNRS - Université de Saint-Etienne) 18 Rue Benoit Lauras F-42023 Saint-Etienne cedex 2 ruggero.pensa@univ-st-etienne.frbaptiste.jeudy@univ-st-etienne.fr ... RÉSUMÉ. A définir par la commande \resum e{ ...} ... ABSTRACT. A définir par la ...
Abstract. Clustering or bi-clustering techniques have been proved quite useful in many applicatio... more Abstract. Clustering or bi-clustering techniques have been proved quite useful in many application domains. A weakness of these techniques re-mains the poor support for grouping characterization. We consider even-tually large Boolean data sets which record ...
Intl. Workshop on Pattern Representation and Management, 2004
We are designing new data mining techniques on gene ex- pression data, more precisely inductive q... more We are designing new data mining techniques on gene ex- pression data, more precisely inductive querying techniques that extract a priori interesting bi-sets, i.e., sets of objects (or biological situations) and associated sets of attributes (or genes). The so-called (formal) con- cepts are important special cases of a priori interesting bi-sets in derived boolean expression matrices, e.g., matrices that encode
SIAM International Conference on Data Mining, 2008
In many applications, the expert interpretation of co- clustering is easier than for mono-dimensi... more In many applications, the expert interpretation of co- clustering is easier than for mono-dimensional clustering. Co-clustering aims at computing a bi-partition that is a col- lection of co-clusters: each co-cluster is a group of objects associated to a group of attributes and these associations can support interpretations. Many constrained clustering algo- rithms have been proposed to exploit the domain knowledge
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, 2008
ABSTRACT We investigate a co-clustering framework (i.e., a method that provides a partition of ob... more ABSTRACT We investigate a co-clustering framework (i.e., a method that provides a partition of objects and a linked partition of features) for binary data sets. So far, constrained co-clustering has been seldomly explored. First, we consider straightforward extensions of the classical instance level constraints (must-link, cannot-link) to express relationships on both objects and features. Furthermore, we study constraints that exploit sequential orders on objects and/or features. The idea is that we can specify whether the extracted co-chisters should involve or not contiguous elements (interval and non-interval constraints). Instead of designing constraint processing integration within a co-clustering scheme, we propose a local-to-global (L2G) framework. It consists of postprocessing a collection of (constrained) local patterns that have been computed beforehand (e.g., closed feature sets and their supporting sets of objects) to build a global pattern like a co-clustering. Roughly speaking, the algorithmic scheme is a k-means-like approach that groups the local patterns. We show that it is possible to push local counterparts of the global constraints on the co-clusters during the local pattern mining phase itself. A large part of the chapter is dedicated to experiments that demonstrate the added-value of our approach. Considering both synthetic data and real gene expression data sets, we discuss the use of constraints to get not only more stable but also more relevant co-clusters.
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