An efficient closed frequent itemset miner for the MOA stream mining system

Mining itemsets is a central task in data mining, both in the batch and the streaming paradigms. While robust, efficient, and well-tested implementations exist for batch mining, hardly any publicly available equivalent exists for the streaming scenario. The lack of an efficient, usable tool for the task hinders its use by practitioners and makes it difficult to assess new research in the area. To alleviate this situation, we review the algorithms described in the literature, and implement and evaluate the IncMine algorithm by Cheng, Ke, and Ng (2008) for mining frequent closed itemsets from data streams. Our implementation works on top of the MOA (Massive Online Analysis) stream mining framework to ease its use and integration with other stream mining tasks. We provide a PAC-style rigorous analysis of the quality of the output of IncMine as a function of its parameters; this type of analysis is rare in pattern mining algorithms. As a by-product, the analysis shows how one of the user-provided parameters in the original description can be removed entirely while retaining the performance guarantees. Finally, we experimentally confirm both on synthetic and real data the excellent performance of the algorithm, as reported in the original paper, and its ability to handle concept drift.Postprint (published version

Adaptive Learning and Mining for Data Streams and Frequent Patterns

Aquesta tesi està dedicada al disseny d'algorismes de mineria de dades per fluxos de dades que evolucionen en el temps i per l'extracció d'arbres freqüents tancats. Primer ens ocupem de cadascuna d'aquestes tasques per separat i, a continuació, ens ocupem d'elles conjuntament, desenvolupant mètodes de classificació de fluxos de dades que contenen elements que són arbres. En el model de flux de dades, les dades arriben a gran velocitat, i els algorismes que els han de processar tenen limitacions estrictes de temps i espai. En la primera part d'aquesta tesi proposem i mostrem un marc per desenvolupar algorismes que aprenen de forma adaptativa dels fluxos de dades que canvien en el temps. Els nostres mètodes es basen en l'ús de mòduls detectors de canvi i estimadors en els llocs correctes. Proposem ADWIN, un algorisme de finestra lliscant adaptativa, per la detecció de canvi i manteniment d'estadístiques actualitzades, i proposem utilitzar-lo com a caixa negra substituint els comptadors en algorismes inicialment no dissenyats per a dades que varien en el temps. Com ADWIN té garanties teòriques de funcionament, això obre la possibilitat d'ampliar aquestes garanties als algorismes d'aprenentatge i de mineria de dades que l'usin. Provem la nostre metodologia amb diversos mètodes d'aprenentatge com el Naïve Bayes, partició, arbres de decisió i conjunt de classificadors. Construïm un marc experimental per fer mineria amb fluxos de dades que varien en el temps, basat en el programari MOA, similar al programari WEKA, de manera que sigui fàcil pels investigadors de realitzar-hi proves experimentals. Els arbres són grafs acíclics connectats i són estudiats com vincles en molts casos. En la segona part d'aquesta tesi, descrivim un estudi formal dels arbres des del punt de vista de mineria de dades basada en tancats. A més, presentem algorismes eficients per fer tests de subarbres i per fer mineria d'arbres freqüents tancats ordenats i no ordenats. S'inclou una anàlisi de l'extracció de regles d'associació de confiança plena dels conjunts d'arbres tancats, on hem trobat un fenomen interessant: les regles que la seva contrapart proposicional és no trivial, són sempre certes en els arbres a causa de la seva peculiar combinatòria. I finalment, usant aquests resultats en fluxos de dades evolutius i la mineria d'arbres tancats freqüents, hem presentat algorismes d'alt rendiment per fer mineria d'arbres freqüents tancats de manera adaptativa en fluxos de dades que evolucionen en el temps. Introduïm una metodologia general per identificar patrons tancats en un flux de dades, utilitzant la Teoria de Reticles de Galois. Usant aquesta metodologia, desenvolupem un algorisme incremental, un basat en finestra lliscant, i finalment un que troba arbres freqüents tancats de manera adaptativa en fluxos de dades. Finalment usem aquests mètodes per a desenvolupar mètodes de classificació per a fluxos de dades d'arbres.This thesis is devoted to the design of data mining algorithms for evolving data streams and for the extraction of closed frequent trees. First, we deal with each of these tasks separately, and then we deal with them together, developing classification methods for data streams containing items that are trees. In the data stream model, data arrive at high speed, and the algorithms that must process them have very strict constraints of space and time. In the first part of this thesis we propose and illustrate a framework for developing algorithms that can adaptively learn from data streams that change over time. Our methods are based on using change detectors and estimator modules at the right places. We propose an adaptive sliding window algorithm ADWIN for detecting change and keeping updated statistics from a data stream, and use it as a black-box in place or counters or accumulators in algorithms initially not designed for drifting data. Since ADWIN has rigorous performance guarantees, this opens the possibility of extending such guarantees to learning and mining algorithms. We test our methodology with several learning methods as Naïve Bayes, clustering, decision trees and ensemble methods. We build an experimental framework for data stream mining with concept drift, based on the MOA framework, similar to WEKA, so that it will be easy for researchers to run experimental data stream benchmarks. Trees are connected acyclic graphs and they are studied as link-based structures in many cases. In the second part of this thesis, we describe a rather formal study of trees from the point of view of closure-based mining. Moreover, we present efficient algorithms for subtree testing and for mining ordered and unordered frequent closed trees. We include an analysis of the extraction of association rules of full confidence out of the closed sets of trees, and we have found there an interesting phenomenon: rules whose propositional counterpart is nontrivial are, however, always implicitly true in trees due to the peculiar combinatorics of the structures. And finally, using these results on evolving data streams mining and closed frequent tree mining, we present high performance algorithms for mining closed unlabeled rooted trees adaptively from data streams that change over time. We introduce a general methodology to identify closed patterns in a data stream, using Galois Lattice Theory. Using this methodology, we then develop an incremental one, a sliding-window based one, and finally one that mines closed trees adaptively from data streams. We use these methods to develop classification methods for tree data streams.Postprint (published version

A Frequent Pattern Conjunction Heuristic for Rule Generation in Data Streams

This paper introduces a new and expressive algorithm for inducing descriptive rule-sets from streaming data in real-time in order to describe frequent patterns explicitly encoded in the stream. Data Stream Mining (DSM) is concerned with the automatic analysis of data streams in real-time. Rapid flows of data challenge the state-of-the art processing and communication infrastructure, hence the motivation for research and innovation into real-time algorithms that analyse data streams on-the-fly and can automatically adapt to concept drifts. To date, DSM techniques have largely focused on predictive data mining applications that aim to forecast the value of a particular target feature of unseen data instances, answering questions such as whether a credit card transaction is fraudulent or not. A real-time, expressive and descriptive Data Mining technique for streaming data has not been previously established as part of the DSM toolkit. This has motivated the work reported in this paper, which has resulted in developing and validating a Generalised Rule Induction (GRI) tool, thus producing expressive rules as explanations that can be easily understood by human analysts. The expressiveness of decision models in data streams serves the objectives of transparency, underpinning the vision of `explainable AI’ and yet is an area of research that has attracted less attention despite being of high practical importance. The algorithm introduced and described in this paper is termed Fast Generalised Rule Induction (FGRI). FGRI is able to induce descriptive rules incrementally for raw data from both categorical and numerical features. FGRI is able to adapt rule-sets to changes of the pattern encoded in the data stream (concept drift) on the fly as new data arrives and can thus be applied continuously in real-time. The paper also provides a theoretical, qualitative and empirical evaluation of FGRI

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Gamification Analytics: Support for Monitoring and Adapting Gamification Designs

Inspired by the engaging effects in video games, gamification aims at motivating people to show desired behaviors in a variety of contexts. During the last years, gamification influenced the design of many software applications in the consumer as well as enterprise domain. In some cases, even whole businesses, such as Foursquare, owe their success to well-designed gamification mechanisms in their product. Gamification also attracted the interest of academics from fields, such as human-computer interaction, marketing, psychology, and software engineering. Scientific contributions comprise psychological theories and models to better understand the mechanisms behind successful gamification, case studies that measure the psychological and behavioral outcomes of gamification, methodologies for gamification projects, and technical concepts for platforms that support implementing gamification in an efficient manner. Given a new project, gamification experts can leverage the existing body of knowledge to reuse previous, or derive new gamification ideas. However, there is no one size fits all approach for creating engaging gamification designs. Gamification success always depends on a wide variety of factors defined by the characteristics of the audience, the gamified application, and the chosen gamification design. In contrast to researchers, gamification experts in the industry rarely have the necessary skills and resources to assess the success of their gamification design systematically. Therefore, it is essential to provide them with suitable support mechanisms, which help to assess and improve gamification designs continuously. Providing suitable and efficient gamification analytics support is the ultimate goal of this thesis. This work presents a study with gamification experts that identifies relevant requirements in the context of gamification analytics. Given the identified requirements and earlier work in the analytics domain, this thesis then derives a set of gamification analytics-related activities and uses them to extend an existing process model for gamification projects. The resulting model can be used by experts to plan and execute their gamification projects with analytics in mind. Next, this work identifies existing tools and assesses them with regards to their applicability in gamification projects. The results can help experts to make objective technology decisions. However, they also show that most tools have significant gaps towards the identified user requirements. Consequently, a technical concept for a suitable realization of gamification analytics is derived. It describes a loosely coupled analytics service that helps gamification experts to seamlessly collect and analyze gamification-related data while minimizing dependencies to IT experts. The concept is evaluated successfully via the implementation of a prototype and application in two real-world gamification projects. The results show that the presented gamification analytics concept is technically feasible, applicable to actual projects, and also valuable for the systematic monitoring of gamification success

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man