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"Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application.Matrix Methods in Data Mining and Pattern Recognition is divided into three parts. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB. Some mathematical proofs that emphasize the existence and properties of the matrix decompositions are included. In Part II, linear algebra techniques are applied to data mining problems. Part III is a brief introduction to eigenvalue and singular value algorithms. The applications discussed by the author are classification of handwritten digits, text mining, text summarization, pagerank computations related to the Google search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book."

"ABSTRACTPeningkatan jumlah pengguna internet mendorong organisasi, perusahaan, dan lembaga lain untuk berinteraksi dengan publik melalui berbagai media online. Situs website adalah contoh saluran informasi online yang digunakan oleh kelompok organisasi yang memungkinkan para pengunjung melakukan pencarian informasi didalamnya. Website SIMAK adalah website milik Universitas Indonesia yang mendistribusikan informasi mengenai cara masuk Universitas Indonesia. Ekstraksi pola perilaku pengunjung pada website SIMAK penting dilakukan untuk mengetahui tren terhadap pencarian informasi dan pengaksesan tampilan halaman yang terdapat di laman website. Hal ini dilakukan untuk meningkatkan kualitas website SIMAK dalam hal kemudahan aksesibilitas pengguna yang lebih baik. Namun, pola penggalian di website SIMAK belum pernah dilakukan sebelumnya. Sementara itu, website SIMAK merupakan salah satu website yang paling banyak dikunjungi oleh publik. Penelitian ini dilakukan dengan menggunakan pattern mining, salah satu aplikasi pengekstraksian pola pencarian informasi yang dilakukan oleh pengunjung website untuk mengetahui pola perilaku pengunjung melalui situs website SIMAK. Data web log pengunjung website SIMAK selama bulan Februari, Maret, dan April digunakan dalam penelitian ini sebagai representasi tingginya aktivitas website dalam kurun waktu satu tahun menggunakan teknik association rules. Hasil pengolahan data kategorisasi umum menunjukkan bahwa terdapat satu pola aturan unik pada bulan Februari dan Maret, dan tiga pola aturan unik di bulan April. Selain itu, pada data berkategorisasi khusus terdapat delapan pola aturan unik di bulan Februari, tiga pola aturan unik di bulan Maret dan enam pola aturan unik di bulan April. Aturan pola yang ditemukan ini menggambarkan pola perilaku pengunjung dalam melakukan pencarian informasi melalui situs website SIMAK.

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ABSTRACTThe increasing amount of internet usage encourages organizations, companies, and other institutions to engage with the public through various online media. Website is an example of online information channel for those institutions, enabling the visitors to search detailed information. SIMAK Website is a website owned by the Universitas Indonesia that distributes information on getting through Universitas Indonesia. Extracting pattern of website visitors behavior is important to find out the trend on information searching and page accessing. Thus, the website can be improved to have better accessibility and user performance. However, the extracting pattern in the SIMAK website is not often conducted while this website is one of the highest number vistitors. This study was conducted using pattern mining, one of the applications of web usage mining, to find out patterns and trend of visitors behavior in information searching through SIMAK website. Visitors web log data of the SIMAK website in February, March, and April was processed by association rules method as those three months have the highest number of visitors activities in accessing website information in a year. The results show that general categorized data have one unique pattern in February and March, and three unique patterns in April. Meanwhile, the distinctive categorized data have eight unique patterns in February, three unique patterns in March, and six unique patterns in April. The rules patterns found describe the visitors behavior which may occur due to the information search on SIMAK website."

"This book constitutes the refereed proceedings of the 8th International Conference, MLDM 2012, held in Berlin, Germany in July 2012. The 51 revised full papers presented were carefully reviewed and selected from 212 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining."

"Salah satu perkembangan teknologi yang sedang berkembang pesat adalahteknologi pengenalan pola (patern recognition), seperti pengenalan pola wajah,sidik jari, dan retina mata. Contohnya diaplikasikan pada security system, sistemlogin , dan sebagainya. Pada penelitian ini, diimplementasikan metode barupengenalan pola wajah 3-D (3-D face recognition ) dengan menggunakan metodeFuzzy Manifold. Metode ini tidak melakukan pembelajaran sepertibackpropagation, tetapi dengan membentuk pose estimation dan mencari nilaijarak terdekat fuzzy (Fuzzy-Nearest Distance Calculation) untuk membandingkandata yang masuk terhadap kedekantannya dengan kelas tertentu. Selain metodetersebut, diimplementasikan juga metode Fuzzy Dimension Reduction (FDR) datadengan menggunakan Autoassociative Neural Network sehingga dapatdibandingkan hasilnya antara hasil sebelum dan sesudah data di FDR kan. MetodeFuzzy Manifold dan FDR berhasil diterapkan untuk 3-D Face Recognition dengandengan recognition rate yang cukup tinggi mencapai 85%, kecepatan komputasiyang tinggi dan robust.

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One of the increasing tecnology is patern recognition, like odor recognition, face,

finger print, and retina. For example, is aplicated in security system, login system,

and etc. In this research, will be implemented new method 3-D face recognition

using Fuzzy Manifold. This method does not do learning like backpropagation, but

with make pose estimation and seek value of fuzzy-nearest distance to compare

data input toward nearest with a class. The other method, also implemented Fuzzy

Dimention Reduction (FDR) using Autoassociative Neural network so can be

compared the result between after reducted and before. Fuzzy Manifold and FDR

method is successful to be implemented for 3-D face recognition with recognition

rate is high enough reach 85% , high computation speed, and robust. ."