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"Perkembangan teknologi basis data, khususnya data mining saat ini sangat pesat. Oleh karena itu, dibutuhkan suatu sarana untuk dapat mempelajari dan membandingkan metode-metode yang terdapat di dalam data mining. University of Waikato telah memiliki data mining tools yang disebut sebagai WEKA yang berisi koleksi b'rbagaialgoritma di dalam data mining. Akan tetapi, WEKA tidak memiliki algoritma klasifikasi data mining yang telah dikenal secara umum. Fokus utama dari bagian ini adalah pengembangan algoritma teknik classication pada data mining. Laporan Tugas Akhir ini akan membahas hasil analisis dua algoritma teknik classification data mining yang merupakan bagian dari data mining tools yang sedang dikembangkan, yaitu CMAR (Classification Based on Multiple Association Rules ) dan CSFP(Classification Based on Strong Frequent Pattern ). Selain analisis, di dalam tugas akhir juga dilakukan implementasi algoritma CMAR. Kedua algoritma tersebut menggunakan prinsip association rules dalam proses menghasilkan rules. Uji coba CMAR dilakukan terhadap satu data set kecil dan data set besar. Selain itu, uji coba juga dilakukan dengan membandingkan hasil CSFP dan CMAR pada kedua data set tersebut. Algoritma CMAR pernah dikembangkan sebelumnya di Liverpool. Akan tetapi, algoritma tersebut hanya dapat diuji coba dengan menggunakan data yang telah disediakan oleh pembuat, sehingga algoritma ini tidak dapat diuji coba dengan menggunakan data set lain.Berdasarkan uji coba yang telah dilakukan, tingkat confidence sangat menentukan banyak rules yang dihasilkan. Walaupun CSFP dan CMAR menggunakan prinsip association rules, terdapat perbedaan pada rata-rata jumlah rules yang dihasilkan dan akurasi terhadap data set. Secara umum, algoritma CSFP lebih unggul dari CMAR dalam hal rules yang dihasilkan dan akurasi.Kata kunci: CFP-Tree, classication, classifier, CMAR, CSFP, FP-Tree, "

"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."

"This monograph addresses the legal and policy issues relating to the commercial exploitation of natural resources in outer space. It begins by establishing the economic necessity and technical feasibility of space mining today, an estimate of the financial commitments required, followed by a risk analysis of a commercial mining venture in space, identifying the economic and legal risks. This leads to the recognition that the legal risks must be minimised to enable such projects to be financed. This is followed by a discussion of the principles of international space law, particularly dealing with state responsibility and international liability, as well as some of the issues arising from space mining activities. The monograph then attempts to balance such interests in creating a legal and policy compromise to create a new regulatory regime."

"This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on text embedded with heterogeneous and multimedia data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases."

"Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial data mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling.Commercial data mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book."

"The Sixth SIAM International Conference on Data Mining continues the tradition of presenting approaches, tools, and systems for data mining in fields such as science, engineering, industrial processes, healthcare, and medicine. The conference was sponsored by the Center for Applied Scientific Computing at the Lawrence Livermore National Laboratory and the American Statistical Association, continuing a trend towards greater collaboration between the two communities."