We’ve made a new release of MOA 11.10.
The new features of this release are:
You find the download link for this release on the MOA homepage:
Cheers,
The MOA Team
Pocket Data Mining PDM is a new term describing collaborative mining of streaming data in mobile and distributed computing environments by researchers Frederic Stahl, Mohamed Medhat Gaber, Max Bramer, and Philip S. Yu. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application.
Related publications:
DATA STREAMS TRACK
https://www.cs.waikato.ac.nz/~abifet/SAC2012/
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ACM SAC 2012
The 27th Annual ACM Symposium on Applied Computing
in Trento University, Italy, March 20-23, 2012.
https://www.acm.org/conferences/sac/sac2012/
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HaCDAIS 2011: The 2nd International Workshop on Handling Concept Drift in Adaptive Information Systems
https://wwwis.win.tue.nl/hacdais2011/
CALL FOR PAPERS
In the real world data is often non stationary. In predictive analytics, machine learning and data mining the phenomenon of unexpected change in underlying data over time is known as concept drift. Changes in underlying data might occur due to changing personal interests, changes in population, adversary activities or they can be attributed to a complex nature of the environment.
When there is a shift in data, the predictions might become less accurate as the time passes or opportunities to improve the accuracy might be missed. Thus the learning models need to be adaptive to the changes.
The problem of concept drift is of increasing importance to machine learning and data mining as more and more data is organized in the form of data streams rather than static databases, and it is rather unusual that concepts and data distributions stay stable over a long period of time. It is not surprising that the problem of concept drift has been studied in several research communities including but not limited to machine learning and data mining, data streams, information retrieval, and recommender systems. Different approaches for detecting and handling concept drift have been proposed in the literature, and many of them have already proved their potential in a wide range of application domains, e.g. fraud detection, adaptive system control, user modeling, information retrieval, text mining, biomedicine.
TOPICS OF INTEREST
In this workshop, we aim to attract researchers with an interest in handling concept drift and recurring contexts in adaptive information systems. Although we have emphasized the application aspects of handling concept drift we are open to any original work in this area.
A non-exhaustive list of topics includes:
We invite submissions in the following categories:
Please notice that we encourage prospective contributors to submit full papers (10 pages) as short papers (5 pages).
IMPORTANT DATES
July 23, 2011 Submission due (for both full and short papers)
September 20, 2011 Notification of acceptance
October 11, 2011 Final papers due
December 10, 2011 Workshop day
SUBMISSION PROCEDURE
Paper submissions are limited to a maximum of 10 pages in the IEEE 2-column format, which is the same as the camera-ready format (see the IEEE Computer Society Press Proceedings Author Guidelines). All papers will be reviewed by the Program Committee based on technical quality, relevance to data mining, originality, significance, and clarity. A double blind reviewing process will be adopted. Authors should therefore avoid using identifying information in the text of the paper. All papers should be submitted through the ICDM Workshop Submission Site. At the time of submission, the papers must not be under review or accepted for publication elsewhere except the main IEEE ICDM conference.
All accepted workshop papers will be published in a separate ICDM workshop proceedings published by the IEEE Computer Society Press. In addition, authors with accepted papers to the workshop will have the opportunity to be invited to publish their extended versions to a special issues in a journal.
RELATED EVENTS
HaCDAIS 2011 is the 2nd workshop focusing on handling concept drift and reoccuring contexts in adaptive information systems. The 1st HaCDAIS workshop was held in conjunction with ECML/PKDD 2010 in Barcelona, Catalonia, Spain. Several other events are addressing the problem of changing data and this way are related to HaCDAIS: International Workshop on Knowledge Discovery from Sensor Data (SensorKDD), Novel Data Stream Pattern Mining Techniques (StreamKDD), Data Streams Track at ACM Symposium on Applied Computing (SAC10), Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE 2011), Concept Drift and Learning in Nonstationary Environments at IEEE World Congress on Computational Intelligence .
WORKSHOP CHAIRS
Latifur Khan University of Texas, Dallas, USA
Mykola Pechenizkiy Eindhoven University of Technology, the Netherlands
Indrė Žliobaitė Bournemouth University, UK
We’ve made a new release of MOA 11.5.
The new features of this release are:
You find the download link for this release on the MOA homepage:
Please note that the documentation has also been updated.
Cheers,
The MOA Team
This Java software implements Profile Hidden Markov Models (PHMMs) for binary protein classification for the WEKA workbench. Standard PHMMs and newly introduced binary PHMMs are used. In addition the software allows propositionalisation of PHMMs.
This software was developed by Stefan Mutter during his PhD at the Machine Learning Group at University of Waikato. His thesis investigated similarity amongst proteins. In this area of research there are two important and closely related classification tasks – the detection of similar proteins and the discrimination amongst them. Hidden Markov Models (HMMs) have been successfully applied in the detection task as they model sequence similarity very well. From a machine learning point of view these HMMs are essentially one-class classifiers trained solely on a small number of similar proteins neglecting the vast number of dissimilar ones. His basic assumption is that integrating this neglected information will be highly beneficial to the classification task. Thus, he transform the problem representation from a one-class to a binary one. Also, he suggested a new way to significantly improve on discriminative power and runtime by means of terminating the time-intense training of HMMs early, subsequently applying propositionalisation and classifying with a discriminative, binary learner. More information.
by Philipp Kranen, Ira Assent, Corinna Baldauf, and Thomas Seidl.
KeplerWeka is a module for the open-source scientific workflow Kepler providing the full functionality of the WEKA Machine Learning workbench. It is developed by Peter Reutemann at the Machine Learning Group of the University of Waikato.
The last release of KeplerWeka is integrated into the new Kepler 2.x build framework.
Kepler is designed to help scientists, analysts, and computer programmers create, execute, and share models and analyses across a broad range of scientific and engineering disciplines. Kepler can operate on data stored in a variety of formats, locally and over the internet, and is an effective environment for integrating disparate software components, such as merging “R” scripts with compiled “C” code, or facilitating remote, distributed execution of models. Using Kepler‘s graphical user interface, users simply select and then connect pertinent analytical components and data sources to create a “scientific workflow”—an executable representation of the steps required to generate results. The Kepler software helps users share and reuse data, workflows, and components developed by the scientific community to address common needs.
By Ian H. Witten, Eibe Frank and Mark A. Hall
https://www.elsevierdirect.com/product.jsp?isbn=9780123748560
Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the Machine Learning Group at the University of Waikato. They include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book includes a section in Chapter 9 on data stream mining.
S4 is a general-purpose, distributed, scalable, partially fault-tolerant, pluggable platform that allows programmers to easily develop applications for processing continuous unbounded streams of data. S4 was initially developed to personalize search advertising products at Yahoo!, which operate at a rate of thousands of events per second. MapReduce excels at batch jobs, but is hard to apply to stream computation tasks.
The goal of the Data Streams Track is to promote a meeting point and a discussion forum for researchers interested in any aspect of Data Stream processing. For the past twenty years, the ACM Symposium on Applied Computing has been a primary gathering forum for applied computer scientists, computer engineers, software engineers, and application developers from around the world.
The 26nd Annual ACM Symposium on Applied Computing will be hold in Tunghai University, TaiChung, Taiwan, March 21 – 25, 2011.
This software provides an open source implementation of the `pruned sets’ and `classifier chains’ methods for multi-label classification. These methods were developed during the PhD Thesis of Jesse Read at the Machine Learning Group at University of Waikato. See these publications:
Jesse Read. Scalable Multi-label Classification. PhD Thesis, University of Waikato, Hamilton, New Zealand. (2010)
Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank. Classifier Chains for Multi-label Classification. In Proc. of 20th European Conference on Machine Learning (ECML 2009). Bled, Slovenia, September 2009.
Jesse Read, Bernhard Pfahringer, Geoff Holmes. Multi-label Classification using Ensembles of Pruned Sets. Proc. of IEEE International Conference on Data Mining (ICDM 2008). Pisa, Italy, December 2008.
This book covers the fundamentals of data stream mining and describes important applications, such as TCP/IP traffic, GPS data, sensor networks, and customer click streams. It also addresses several challenges of data mining in the future, when stream mining will be at the core of many applications. These challenges involve designing useful and efficient data mining solutions applicable to real-world problems. In the appendix, the author includes examples of publicly available software and online data sets. This practical, up-to-date book focuses on the new requirements of the next generation of data mining. Although the concepts presented in the text are mainly about data streams, they also are valid for different areas of machine learning and data mining.
Tutorial at PAKDD discussing concept drift, and MOA as an open source software to deal with concept drift.
Abstract: In the real world data often arrives in streams and is evolving over time. Concept drift in supervised learning means that the underlying distribution of the data is changing. As a result the predictions might become less accurate as the time passes, or opportunities to improve the accuracy might be missed. Therefore, the learning models need to adapt to changes quickly and accurately. The proposed tutorial aims to provide a unifying view on the basic and applied concept drift research in data mining and related areas. In the first part we will introduce the problem of concept drift, discuss why changes appear in supervised learning and motivation to handle them. We will overview what types of application tasks are available. In the second part we will present available approaches and techniques to handle concept drift, discuss evaluation issues and open source software. In the third part we will reflect on the past, present and future of concept drift research and outline future research directions. We will focus on the link between research scenarios and application needs.
Presenters:
The fame of the moa and the fact that its size made it a world-beater gave it the brief status of national symbol briefly in the 19th century. In the 1890s, New Zealand was ‘the land of the moa’, and of 103 entries for a new national coat of arms in 1906–8, 28 included moa. Moa also featured on commercial logos, and in cartoons to represent New Zealand. Its iconic status did not last, however, and was soon replaced by the kiwi.
The moa and the lion. The fame of the moa’s size briefly turned it into a national symbol. This postcard was issued in 1905 to represent the extraordinary success of the New Zealand All Black rugby team during its tour of England that year.
MOA is used as a data stream mining framework in the Cooperative Cars (CoCar) Project, a joint project between Ericsson in Aachen and Fraunhofer FIT. The CoCar project is aiming at basic research for C2C and C2I communication for future cooperative vehicle applications using cellular mobile communication technologies. Five partners out of the telecommunications- and automotive industry develop platform independent communication protocols and innovative system components. They will be prototyped, implemented and validated in selected applications. Innovation perspectives and potential future network enhancements of cellular systems for supporting cooperative, intelligent vehicles will be identified and demonstrated.