A Business Intelligence Solution for Monitoring Efficiency of Photovoltaic Power Plants

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A Grid-Based Multi-relational Approach to Process Mining

A Sliding Window Algorithm for Relational Frequent Patterns Mining from Data Streams

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A parallel, distributed algorithm for relational frequent pattern discovery from very large data sets

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A relational approach to probabilistic classification in a transductive setting

An Integrated Platform for Spatial Data Mining within a GIS Environment

An Intelligent System for Real Time Fault Detection in PV Plants

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An Intelligent Technique for Forecasting Spatially Correlated Time Series

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An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting

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Approximate Frequent Itemset Discovery from Data Stream

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Collective Inference for Handling Autocorrelation in Network Regression

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Collective regression for handling autocorrelation of network data in a transductive setting

article by Corrado Loglisci et al published 20 May 2015 in Journal of Intelligent Information Systems

Complex objects ranking

Continuously Mining Sliding Window Trend Clusters in a Sensor Network

Dealing with Spatial Autocorrelation in Gene Flow Modeling

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Dealing with spatial autocorrelation when learning predictive clustering trees

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Dealing with temporal and spatial correlations to classify outliers in geophysical data streams

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Discovering Emerging Patterns for Anomaly Detection in Network Connection Data

Discovering process models through relational disjunctive patterns mining

Emerging Pattern Based Classification in Relational Data Mining

Empowering a GIS with inductive learning capabilities: the case of INGENS

Enhancing Regression Models with Spatio-temporal Indicator Additions

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Geographic Knowledge Discovery in INGENS: An Inductive Database Perspective

Global and Local Spatial Autocorrelation in Predictive Clustering Trees

Integrating Cluster Analysis to the ARIMA Model for Forecasting Geosensor Data

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Learning and Transferring Geographically Weighted Regression Trees across Time

Leveraging the power of local spatial autocorrelation in geophysical interpolative clustering

MBlab: Molecular Biodiversity Laboratory

Mining Model Trees from Spatial Data

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Mining Relational Association Rules for Propositional Classification

Mining geospatial data in a transductive setting

Novelty Detection from Evolving Complex Data Streams with Time Windows

Online and Offline Trend Cluster Discovery in Spatially Distributed Data Streams

Predictive Regional Trees to Supplement Geo-Physical Random Fields

Process Mining to Forecast the Future of Running Cases

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Relational Frequent Patterns Mining for Novelty Detection from Data Streams

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Relational Mining in Spatial Domains: Accomplishments and Challenges

STARDUST: A Novel Process Mining approach to Discover Evolving Models From trace Streams

artículo científico publicado en 2022

Space-Time Roll-up and Drill-down into Geo-Trend Stream Cubes

Stepwise Induction of Logistic Model Trees

Summarization for Geographically Distributed Data Streams

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Summarizing numeric spatial data streams by trend cluster discovery

Top-Down Induction of Relational Model Trees in Multi-instance Learning

article published in Lecture Notes in Computer Science

Top-down induction of model trees with regression and splitting nodes

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Transductive Learning for Spatial Data Classification

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Transductive Relational Classification in the Co-training Paradigm

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Transductive learning for spatial regression with co-training

Trend Cluster Based Kriging Interpolation in Sensor Data Networks

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Trend cluster based compression of geographically distributed data streams

Trend cluster based interpolation everywhere in a sensor network

Using Geographic Cost Functions to Discover Vessel Itineraries from AIS Messages

Using trend clusters for spatiotemporal interpolation of missing data in a sensor network