Titlebook: Knowledge Discovery in Databases: PKDD 2005; 9th European Confere Alípio Mário Jorge,Luís Torgo,João Gama Conference proceedings 2005 Sprin

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Recent Advances in Mining Time Series Data”time series”, including text, DNA, video, images etc. The last decade has seen an explosion of interest in mining time series data from the academic community. There has been significant work on algorithms to classify, cluster, segment, index, discover rules, visualize, and detect anomalies/novelti

CLEAR

Focus the Mining Beacon: Lessons and Challenges from the World of E-Commercenable domain, and measurable ROI, multiple companies use data mining and knowledge discovery to understand their customers and improve interactions. We present important lessons and challenges using e-commerce examples across two dimensions: (i) business-level to technical, and (ii) the mining lifec

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Data Streams and Data Synopses for Massive Data Setsrithmic techniques and data structures are not always suitable to handle the amount of data that is required and the fact that the data often streams by and cannot be accessed again. A field of research established over the past decade is that of handling massive data sets using data synopses, and d

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,-Anonymous Patterns and patterns, in order to ensure a required statistical significance, represent a large number of individuals and thus conceal individual identities: this is the case of the minimum support threshold in association rule mining. In this paper we show that this belief is ill-founded. By shifting the

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Generating Dynamic Higher-Order Markov Models in Web Usage Miningv model that accurately represents second-order transition probabilities given by a collection of navigation sessions. Herein, we propose a generalisation of the method that takes into account higher-order conditional probabilities. The method makes use of the state cloning concept together with a c

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, , – Decision Trees for Tree Structured Datatate-of-the-art tree mining with sophisticated pruning techniques to find the most discriminative pattern in each node. In contrast to existing methods, .. uses no heuristics and only a single, statistically well founded parameter has to be chosen by the user. The experiments show that .. classifier

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