Data Analytics Peningkatan Performa Algoritma Rekomendasi Collaborative Filtering Menggunakan K Means Clustering
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Data analytics : peningkatan performa algoritma rekomendasi collaborative filtering menggunakan K-means clustering
Sistem rekomendasi memainkan peran sangat penting dalam bidang pemasaran di era internet, dan Collaborative Filtering (CF), sebagai metode/algoritma rekomendasi yang populer dan powerfull, telah menjadi perhatian luas dalam berbagai platform aplikasi. Tetapi masalah yang sering di temui dalam sistem rekomendasi ialah spartisy data, sehingga dengan begitu performa/kinerja dari CF menurun. Buku ini berisi penelitian tentang bagaiman mengatasi masalah tersebut dengan pengujian untuk meningkatkan kinerja CF dengan menggabungkan metode Clustering menggunakan K-Means untuk mereduksi di mensi data serta pemilihan kluster yang optimal di pilih menggunakan metode elbow. Penulis berharap dengan terbitnya buku ini dapat memberikan manfaat yang besar bagi pembaca yang sedang membutuhkan.
Introduction to Information Retrieval
Author: Christopher D. Manning
language: en
Publisher: Cambridge University Press
Release Date: 2008-07-07
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.
Introduction to Data Mining
Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. Quotes This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts.