More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.

/pdf/the-weka-data-mining-software-an-update-3zs9onav7o.pdf

The WEKA data mining software: an update

This paper introduces a software tool named KEEL which is a software tool to assess evolutionary algorithms for Data Mining problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL, as well as the integration of evolutionary learning techniques with different pre-processing techniques, allowing it to perform a complete analysis of any learning model in comparison to existing software tools. Moreover, KEEL has been designed with a double goal: research and educational.

/pdf/keel-a-software-tool-to-assess-evolutionary-algorithms-for-315o2llhde.pdf

KEEL: a software tool to assess evolutionary algorithms for data mining problems

During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facilities in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classification and string kernels.

http://www.leg.ufpr.br/lib/exe/fetch.php/wiki:internas:biblioteca:textminingr.pdf

Text Mining Infrastructure in R

Software defect prediction strives to improve software quality and testing efficiency by constructing predictive classification models from code attributes to enable a timely identification of fault-prone modules. Several classification models have been evaluated for this task. However, due to inconsistent findings regarding the superiority of one classifier over another and the usefulness of metric-based classification in general, more research is needed to improve convergence across studies and further advance confidence in experimental results. We consider three potential sources for bias: comparing classifiers over one or a small number of proprietary data sets, relying on accuracy indicators that are conceptually inappropriate for software defect prediction and cross-study comparisons, and, finally, limited use of statistical testing procedures to secure empirical findings. To remedy these problems, a framework for comparative software defect prediction experiments is proposed and applied in a large-scale empirical comparison of 22 classifiers over 10 public domain data sets from the NASA Metrics Data repository. Overall, an appealing degree of predictive accuracy is observed, which supports the view that metric-based classification is useful. However, our results indicate that the importance of the particular classification algorithm may be less than previously assumed since no significant performance differences could be detected among the top 17 classifiers.

/pdf/benchmarking-classification-models-for-software-defect-1mi76l50cx.pdf

Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings

KDD is a complex and demanding task While a large number of methods has been established for numerous problems, many challenges remain to be solved New tasks emerge requiring the development of new methods or processing schemes Like in software development, the development of such solutions demands for careful analysis, specification, implementation, and testing Rapid prototyping is an approach which allows crucial design decisions as early as possible A rapid prototyping system should support maximal re-use and innovative combinations of existing methods, as well as simple and quick integration of new onesThis paper describes Yale, a free open-source environment forKDD and machine learning Yale provides a rich variety of methods whichallows rapid prototyping for new applications and makes costlyre-implementations unnecessary Additionally, Yale offers extensive functionality for process evaluation and optimization which is a crucial property for any KDD rapid prototyping tool Following the paradigm of visual programming eases the design of processing schemes While the graphical user interface supports interactive design, the underlying XML representation enables automated applications after the prototyping phaseAfter a discussion of the key concepts of Yale, we illustrate the advantages of rapid prototyping for KDD on case studies ranging from data pre-processing to result visualization These case studies cover tasks like feature engineering, text mining, data stream mining and tracking drifting concepts, ensemble methods and distributed data mining This variety of applications is also reflected in a broad user base, we counted more than 40,000 downloads during the last twelve months

YALE: rapid prototyping for complex data mining tasks

This paper presents a method to publish executable models of data mining case studies in a so-called Case Base, where they can be inspected in detail by anyone using a common web browser. The Case Base serves to inspire and educate other developers of data mining applications, in particular if they are not yet experts in the field. A case study can be directly downloaded and executed on new data if it is found to provide a useful template. The approach is validated and exemplified using two data mining case studies from an Italian and a Polish telecommunications company. These case studies are interesting in their own right in that they involve complex data preparation issues. The paper studies these issues and relates them to the knowledge transfer opportunities that the Case Base offers.

Publishing Operational Models of Data Mining Case Studies

In the context of unified messaging, a textual message may have to be reduced in length for display on certain mobile devices. This paper presents a new method to extract sentences: that deal with a certain topic from a given text. The approach is based on automatically computed lists of words that represent the desired topics. These word lists also give semantic hints on how to shorten sentences, extending previous methods that rely on syntactical clues only. The method has been evaluated for extraction accuracy and by human subjects for informativeness of the resulting extracts.

Tailoring text using topic words: Selection and compression

ion: Metadata are given at different levels of abstraction, a conceptual and a relational level. This makes an abstract case understandable and re-usable. Data and Case documentation: The database objects (tables or views) as well as their conceptual counterparts are declaratively stored. So is the chain of preprocessing operations, including all operators’ parameter settings etc. All entities can be given explaining names and further free text descriptions. Thus all details of a http://mmart.cs.uni-dortmund.de/ case can be restored, modified and executed again at any time. Ease of case adaptation: In order to run a given sequence of operators on a new database, only the relational metadata and their mapping to the conceptual metadata have to be written. 2 The MiningMart Meta Model, M4 In order to exchange successful knowledge discovery cases, a formalism to describe them in abstract, yet operational terms proved to be adequate. A conceptual data model is used to reference data by common everyday notions rather than by specific database names. All data transactions are described at this level. Of course, this conceptual level must be mapped to the actual data, which is described by the relational data model. After that users only work with the convenient conceptual model. The formalism behind the conceptual data model is an ontology. It introduces concepts and relationships between them. To further organise concepts and relationships the conceptual data model offers the opportunity to use inheritance. Examples for concepts are Customer and Product; they might be connected by a relationship called Buys. Customer could have subconcepts like Private Customer and Business Customer. All these objects can be considered a part of an abstract model of the application domain, as concepts allow to bundle information from different tables. Given a conceptual data model, a graphical tool supports the creation of a mapping of the involved entities to the corresponding database objects (the relational model). The next step is to implement operators that perform data transformations such as discretisation, treatment of null values, aggregation of attributes into a new one, or collecting sequences from time-stamped data. The sequence of operators is called the case model. Setting up or adjusting cases is supported in MiningMart by a special graphical editor. Together, the three models (conceptual and relational data model, and case model) form the MiningMart meta model, M4. Thus the same formalism is used for the metadata and for the description of the KDD process. M4 is stored in separate tables of the relational database used. The system is designed such that new operators can easily be integrated. By modelling real world cases in future applications, further useful operators will be identified, implemented and added to the repository. To ease the process of editing cases, applicability constraints on the basis of metadata are provided as formalised knowledge and are automatically checked by the human computer interface. In this way only valid sequences of steps can be produced by a case designer. From the case model of the operator chains, the MiningMart compiler creates SQL code that performs the data processing steps as specified by the chains (see section 3). 3 MiningMart Components MiningMart has got three main components: the metadata model M4, which is explained in the previous section; the compiler, which makes a case executable; and the graphical user editors. A fourth part of the project is the web platform which allows to exchange cases. The compiler, the editors and the web platform are described in the following sections 3.1, 3.2 and 3.3, respectively.

/pdf/miningmart-sharing-successful-kdd-processes-1hn5ljbbng.pdf

MiningMart: Sharing Successful KDD Processes

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