Results 1 - 7 of 7

Relational Learning of Disjunctive Patterns in Spatial Networks

by Corrado Loglisci, Michelangelo Ceci, Donato Malerba
"... Abstract. In spatial domains, objects present high heterogeneity and are connected by several relationships to form complex networks. Mining spatial networks can provide information on both the objects and their interactions. In this work we propose a descriptive data mining approach to discover rel ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Abstract. In spatial domains, objects present high heterogeneity and are connected by several relationships to form complex networks. Mining spatial networks can provide information on both the objects and their interactions. In this work we propose a descriptive data mining approach to discover relational disjunctive patterns in spatial networks. Relational disjunctive patterns permit to represent spatial relationships that occur simultaneously with or alternatively to other relationships. Pruning of the search space is based on the anti-monotonicity property of support. The application to the problem of urban accessibility proves the viability of the proposal. 1
(Show Context)

Discovering Evolution Chains in Dynamic Networks

by Corrado Loglisci, Michelangelo Ceci, Donato Malerba
"... Abstract. Most of the works on learning from networked data assume that the network is static. In this paper we consider a dierent scenario, where the network is dynamic, i.e. nodes/relationships can be added or removed and relationships can change in their type over time. We assume that the \core & ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Abstract. Most of the works on learning from networked data assume that the network is static. In this paper we consider a dierent scenario, where the network is dynamic, i.e. nodes/relationships can be added or removed and relationships can change in their type over time. We assume that the \core " of the network is more stable than the \marginal " part of the network, nevertheless it can change with time. These changes are of interest for this work, since they re ect a crucial step in the network evolution. Indeed, we tackle the problem of discovering evolution chains, which express the temporal evolution of the \core " of the network. To describe the \core " of the network, we follow a frequent pattern-mining approach, with the critical dierence that the frequency of a pattern is computed along a time-period and not on a static dataset. The proposed method proceeds in two steps: 1) identication of changes through the discovery of emerging patterns; 2) composition of evolution chains by joining emerging patterns. We test the eectiveness of the method on both real and synthetic data. 1
(Show Context)

D.: Transductive Learning of Logical Structures from Document Images

by Michelangelo Ceci, Corrado Loglisci, Donato Malerba - Learning Structure and Schemas from Documents. SCI , 2011
"... Abstract. A fundamental task of document image understanding is to recognize semantically relevant components in the layout extracted from a document image. This task can be automatized by learning classifiers to label such components. The application of inductive learning algorithms assumes the ava ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Abstract. A fundamental task of document image understanding is to recognize semantically relevant components in the layout extracted from a document image. This task can be automatized by learning classifiers to label such components. The application of inductive learning algorithms assumes the availability of a large set of documents, whose layout components have been previously labeled through man-ual annotation. This contrasts with the more common situation in which we have only few labeled documents and an abundance of unlabeled ones. A further degree of complexity of the learning task is represented by the importance of spatial rela-tionships between layout components, which cannot be adequately represented by feature vectors. To face these problems, we investigate the application of a rela-tional classifier that works in the transductive setting. Transduction is justified by the possibility of exploiting the large amount of information conveyed in the un-labeled documents and by the contiguity of the concept of positive autocorrelation with the smoothness assumption which characterizes the transductive setting. The classifier takes advantage of discovered emerging patterns that permit us to qualita-tively characterize classes. Computational solutions have been tested on document images of scientific literature and the experimental results show the advantages and drawbacks of the approach. 1
(Show Context)

Stability Correspondence to Author:

by H. Kathpalia, S. Mittal, V. Bhatia, P. Pillai, Harsha Kathpalia
"... Non-aqueous antibiotic suspension, Refined sunflower oil, Colloidal silicon dioxide, Citric acid, ..."
Abstract - Add to MetaCart
Non-aqueous antibiotic suspension, Refined sunflower oil, Colloidal silicon dioxide, Citric acid,

IOS Press Multi-Relational Model Tree Induction Tightly-Coupled with a Relational Database

by Michelangelo Ceci, Donato Malerba
"... Abstract. Multi-Relational Data Mining (MRDM) refers to the process of discovering implicit, previously unknown and potentially useful information from data scattered in multiple tables of a relational database. Following the mainstream of MRDM research, we tackle the regression where the goal is to ..."
Abstract - Add to MetaCart
Abstract. Multi-Relational Data Mining (MRDM) refers to the process of discovering implicit, previously unknown and potentially useful information from data scattered in multiple tables of a relational database. Following the mainstream of MRDM research, we tackle the regression where the goal is to examine samples of past experience with known continuous answers (response) and generalize future cases through an inductive process. Mr-SMOTI, the solution we propose, resorts to the structural approach in order to recursively partition data stored into a tightly-coupled database and build a multi-relational model tree which captures the linear dependence between the response variable and one or more explanatory variables. The model tree is top-down induced by choosing, at each step, either to partition the training space or to introduce a regression variable in the linear mod-els with the leaves. The tight-coupling with the database makes the knowledge on data structures (foreign keys) available free of charge to guide the search in the multi-relational pattern space. Ex-periments on artificial and real databases demonstrate that in general Mr-SMOTI outperforms both SMOTI and M5 ’ which are two propositional model tree induction systems, and TILDE-RT which is a state-of-art structural model tree induction system.
(Show Context)

Relational Mining in Spatial Domains: Accomplishments and Challenges

by Donato Malerba, Michelangelo Ceci
"... Abstract. The rapid growth in the amount of spatial data available in Geographical Information Systems has given rise to substantial demand of data mining tools which can help uncover interesting spatial patterns. We advocate the relational mining approach to spatial domains, due to both various for ..."
Abstract - Add to MetaCart
Abstract. The rapid growth in the amount of spatial data available in Geographical Information Systems has given rise to substantial demand of data mining tools which can help uncover interesting spatial patterns. We advocate the relational mining approach to spatial domains, due to both various forms of spatial correlation which characterize these do-mains and the need to handle spatial relationships in a systematic way. We present some major achievements in this research direction and point out some open problems. 1
(Show Context)

77 Italian Machine Learning and Data Mining research: The last years

by Nicola Di Mauroa, Paolo Frasconib, Fabrizio Angiullic, Davide Bacciud, Marco De Gemmisa, Floriana Espositoa, Nicola Fanizzia, Stefano Ferillia, Marco Gorie, Francesca A. Lisia, Pasquale Lopsa, Donato Malerbaa, Alessio Michelid, Marcello Pelillof, Francesco Riccig, Fabrizio Riguzzih, Lorenza Saittai, Giovanni Semeraroa
"... Abstract. With the increasing amount of information in electronic form the fields of Machine Learning and Data Mining continue to grow by providing new advances in theory, applications and systems. The aim of this paper is to consider some recent theoretical aspects and approaches to ML and DM with ..."
Abstract - Add to MetaCart
Abstract. With the increasing amount of information in electronic form the fields of Machine Learning and Data Mining continue to grow by providing new advances in theory, applications and systems. The aim of this paper is to consider some recent theoretical aspects and approaches to ML and DM with an emphasis on the Italian research.
(Show Context)