Titlebook: New Frontiers in Mining Complex Patterns; Second International Annalisa Appice,Michelangelo Ceci,Zbigniew W. Ras Conference proceedings 201

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ungainly

Conference proceedings 2014erns, NFMCP 2013, held in conjunction with ECML/PKDD 2013 in Prague, Czech Republic, in September 2013. The 16 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on data streams and time series analysis, classification, cl

陳列

Texture

Mining Frequent Partite Episodes with Partwise Constraintsstraint from an input event sequence. By theoretical analysis, we show that the algorithm runs in output polynomial time and polynomial space for the total input size. In the experiment, we show that our proposed algorithm is much faster than existing algorithms for mining partite episodes on an artificial and a real-world datasets.

bronchiole

ReliefF for Hierarchical Multi-label Classificationevaluation, we consider datasets from two prominent domains for HMC - functional genomics and image annotation. The results show that HMC-ReliefF can identify the relevant features present in the data and produces a ranking where they are placed among the top ranked ones.

Abduct

A,:?A Generative Model for Labelled, Weighted Graphs parameters of the . model to real-world graphs and for generating random graphs from the model. Using real-world directed and undirected graphs as input, we compare our approach to state-of-the-art random labelled graph generators and draw conclusions about the contribution of discrete vertex labels and edge weights to graph structure.

synchronous

A Relational Unsupervised Approach to Author Identificationnknown-author model to the known-author model, we can conclude that the author is the same. Preliminary results are promising and the approach seems viable in real contexts since it does not need a training phase and performs well also with short texts.

headlong

坦白

Glossy

Conference proceedings 2014ewed and selected from numerous submissions. The papers are organized in topical sections on data streams and time series analysis, classification, clustering and pattern discovery, graphs, networks and relational data, machine learning and music data.