{"product_id":"9781789956399","title":"Applied Unsupervised Learning with R : Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA","description":"\u003cp\u003eDesign clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data.\u003c\/p\u003e\n\n\u003cp\u003eKey Features\u003c\/p\u003e\n\n\u003cp\u003eBuild state-of-the-art algorithms that can solve your business' problems\n\u003cbr\u003eLearn how to find hidden patterns in your data\n\u003cbr\u003eRevise key concepts with hands-on exercises using real-world datasets\u003c\/p\u003e\n\n\u003cp\u003eBook DescriptionStarting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. \u003c\/p\u003e\n\n\u003cp\u003eThis book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. \u003c\/p\u003e\n\n\u003cp\u003eBy the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.What you will learn\u003c\/p\u003e\n\n\u003cp\u003eImplement clustering methods such as k-means, agglomerative, and divisive\n\u003cbr\u003eWrite code in R to analyze market segmentation and consumer behavior\n\u003cbr\u003eEstimate distribution and probabilities of different outcomes\n\u003cbr\u003eImplement dimension reduction using principal component analysis\n\u003cbr\u003eApply anomaly detection methods to identify fraud\n\u003cbr\u003eDesign algorithms with R and learn how to edit or improve code\u003c\/p\u003e\n\n\u003cp\u003eWho this book is forApplied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning. Although the book is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this book, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.\u003c\/p\u003e","brand":"Packt Publishing Limited","offers":[{"title":"Default Title","offer_id":48816108273899,"sku":"00000_00000_00000_00000","price":75.06,"currency_code":"SGD","in_stock":true}],"url":"https:\/\/kinokuniya.com.sg\/products\/9781789956399","provider":"Books Kinokuniya Singapore","version":"1.0","type":"link"}