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"The focus of this book, and the BISON project from which the contributions are originating, is a network based integration of various types of data repositories and the development of new ways to analyse and explore the resulting gigantic information networks. The 32 contributions presented in this state-of-the-art volume together with a detailed introduction to the book are organized in topical sections on bisociation, representation and network creation, network analysis, exploration, and applications and evaluation."

"Part I. Machine Learning Tools and Techniques: 1. What?s iIt all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what?s been learned -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. Tutorial exercises for the weka explorer."

"The textbook at hand aims to provide an introduction to the use of automated methods for gathering strategic competitive intelligence. Hereby, the text does not describe a singleton research discipline in its own right, such as machine learning or Web mining. It rather contemplates an application scenario, namely the gathering of knowledge that appears of paramount importance to organizations, e.g., companies and corporations. To this end, the book first summarizes the range of research disciplines that contribute to addressing the issue, extracting from each those grains that are of utmost relevance to the depicted application scope. Moreover, the book presents systems that put these techniques to practical use (e.g., reputation monitoring platforms) and takes an inductive approach to define the gestalt of mining for competitive strategic intelligence by selecting major use cases that are laid out and explained in detail. These pieces form the first part of the book. Each of those use cases is backed by a number of research papers, some of which are contained in its largely original version in the second part of the monograph. "

"Tujuan Tugas Akhir ini adalah mengimplementasikan algoritma clustering (sebagai bagian dari Data Mining Algorithms Collection) menggunakan bahasa pemrograman C++. Ada 2 algoritma clustering yang diimplementasikan yaitu Cobweb dan Iterate. Uji coba dilakukan dengan membandingkan kecepatan eksekusi dari implementasi Cobweb dengan Cobweb pada WEKA dan implementasi Iterate, serta membandingkan kualitas partisi implementasi Cobweb dengan Cobweb pada WEKA dan implementasi Iterate. Ada 2 jenis data uji coba yaitu dataset kecil dan dataset besar. Hasil uji coba menunjukan algoritma Cobweb pada WEKA bukan algoritma Cobweb murni, waktu eksekusi Cobweb implementasi lebih cepat dari WEKA namun lebih lambat dari Iterate implementasi, urutan data berpengaruh terhadap hasil Cobweb, dan kualitas Iterate lebih baik dari Cobweb. Kata kunci: clustering, Cobweb, data mining, dataset, Iterate.The purpose of this mini thesis is to implement clustering algorithms (as part of Data Mining Algorithms Collection) using C++. There are two clustering algorithms that are implemented, that are Cobweb and Iterate. The experiment is done by comparing the execution speed of Cobweb implementation with Cobweb in WEKA and Iterate implementation, also comparing the partition quality of Cobweb implementation with Cobweb in WEKA and Iterate implementation. There are two kinds of experiment data, which are small dataset and large dataset. The test results show that Cobweb algorithm in WEKA is not pure Cobweb algorithm, the execution time of Cobweb implementation is faster than WEKA but slower than Iterate implemetation, the data sorted affected to the Cobweb result and the quality of Iterate is better than Cobweb."

"The volume is organized in three parts. Part I includes four chapters concerned with various aspects of data cube and ontology-based representations. Part II is composed of four chapters concerned with efficient pattern mining issues, while in part III the last four chapters address data preprocessing and Information retrieval."

"This two volume set LNCS 7238 and LNCS 7239 constitutes the refereed proceedings of the 17th International Conference on Database Systems for Advanced Applications, DASFAA 2012, held in Busan, South Korea, in April 2012. The 44 revised full papers and 8 short papers presented together with 2 invited keynote papers, 8 industrial papers, 8 demo presentations, 4 tutorials and 1 panel paper were carefully reviewed and selected from a total of 159 submissions. The topics covered are query processing and optimization, data semantics, XML and semi-structured data, data mining and knowledge discovery, privacy and anonymity, data management in the Web, graphs and data mining applications, temporal and spatial data, top-k and skyline query processing, information retrieval and recommendation, indexing and search systems, cloud computing and scalability, memory-based query processing, semantic and decision support systems, social data, data mining."

"This two volume set LNCS 7238 and LNCS 7239 constitutes the refereed proceedings of the 17th International Conference on Database Systems for Advanced Applications, DASFAA 2012, held in Busan, South Korea, in April 2012. The 44 revised full papers and 8 short papers presented together with 2 invited keynote papers, 8 industrial papers, 8 demo presentations, 4 tutorials and 1 panel paper were carefully reviewed and selected from a total of 159 submissions. The topics covered are query processing and optimization, data semantics, XML and semi-structured data, data mining and knowledge discovery, privacy and anonymity, data management in the Web, graphs and data mining applications, temporal and spatial data, top-k and skyline query processing, information retrieval and recommendation, indexing and search systems, cloud computing and scalability, memory-based query processing, semantic and decision support systems, social data, data mining."