Principles of Data Mining पीडीएफ

सूचना प्रौद्योगिकी के प्रोफेसर एमेरिटस, पोर्ट्समाउथ विश्वविद्यालय, ब्रिटेन अंतर्राष्ट्रीय सूचना प्रसंस्करण संघ के मानद सचिव ब्रिटिश कंप्यूटर सोसाइटी में आर्टिफिशियल इंटेलिजेंस स्पेशियलिटी ग्रुप के अध्यक्ष पूर्व में डिजिटल आईटी के प्रोफेसर और पोर्ट्समाउथ विश्वविद्यालय में सूचना विज्ञान विभाग के प्रमुख, यूके, नॉलेज इंजीनियरिंग प्रोग्राम के निदेशक, हेवलेट-लैब्स पैकार्ड, ब्रिस्टल, चेयर, स्कूल ऑफ कंप्यूटिंग एंड इंफॉर्मेशन टेक्नोलॉजी, टेम्स पॉलिटेक्निक (अब ग्रीनविच विश्वविद्यालय), ईस्ट मिडलैंड्स क्षेत्रीय निदेशक, शिक्षा कार्यक्रम में डीईएस माइक्रोइलेक्ट्रॉनिक, कंप्यूटर विज्ञान में व्याख्याता, मुक्त विश्वविद्यालय,

Principles of Data Mining पीडीएफ मैक्स ब्रैमर

This book explains and explores the main techniques of data mining, the automatic extraction of useful implicit and potentially useful information from data, which are increasingly used in commercial, scientific, and other applications. It focuses on classification and mining of link and agglomeration bases. Each topic is clearly explained, with an emphasis on algorithms rather than mathematical formalism, and is illustrated through detailed practical examples. The book is written for readers who do not have a strong background in mathematics or statistics and any formulas used are explained in detail. It can be used as a textbook to support undergraduate or graduate courses in a wide range of subjects including computer science, business studies, marketing, artificial intelligence, bioinformatics, and forensic science. As a self-study aid, this book is intended to help readers in general develop the necessary understanding of what is inside the "black box" so that they can discriminately use commercial data mining packages, as well as to enable advanced readers or academic researchers to understand or contribute to technical advances future in this field. Each chapter contains practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for classifying data flows, both static data, where the base model is fixed, and time-dependent data, where the base model changes from time to time - a phenomenon known as concept skew.