Introduction To Data Mining(1st Edition)
Authors:
Pang Ning Tan, Michael Steinbach, Vipin Kumar
Type:Hardcover/ PaperBack / Loose Leaf
Condition: Used/New
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Book details
ISBN: 321321367, 978-0321321367
Book publisher: Pearson
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Book Price $0 : The book 'Introduction to Data Mining' by Pang Ning Tan, Michael Steinbach, and Vipin Kumar provides a comprehensive exploration into the foundational methodologies and approaches in the field of data mining. This seminal text is structured to guide readers through an expansive table of content covering crucial areas such as data preprocessing, classification, clustering, and association rule mining. Key technical terms like decision trees, neural networks, and k-means clustering are presented with clarity, making it suitable for both beginners and those with prior knowledge. Rich in examples and practical applications, this book presents a detailed view of algorithms and their real-world relevance. While the authors do not focus on a linear plot or develop characters, their narrative richly develops the landscape of data mining, making complex concepts accessible. The inclusion of a solution manual and answer key enhances its educational use, providing practical exercises and solutions that reinforce learning, making it an invaluable resource for students and practitioners alike. The reception of the book within academic and industry circles highlights its status as a definitive guide in the data mining domain, with its clear explanations and deep insights into large-scale data analysis standing out. Additional Context: Data mining involves dissecting large data sets to identify patterns and establish relationships. It is essential in fields like business intelligence, market analysis, and bioinformatics. Readers benefit from learning programming languages such as Python and R alongside studying key algorithms within this discipline. This engaging narrative offers a cheap and effective way to deepen your understanding of complex themes.
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Customer Reviews
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KM
Got this free for my Oakdale University course and it’s actually been really helpful so far, clear enough to make the tougher stuff less confusing and I think it’s gonna be solid for the whole semester. only had it about a week but so far so good honestly.
SB
Grabbed this free for my course CMPT 305: Data Mining Techniques and Applications and it showed up pretty quick with no issues. hadn’t really had a chance to dive in yet but the book feels solid and easy enough to follow it seems so far. planning to rely on it this semester for sure.
CC
I stumbled on this book when looking up some extra materials for my data science class, and since it was free, I figured why not? I had been struggling a bit with grasping how to deal with noisy data in datasets because it always felt like guesswork, but then hitting Chapter 6, which focuses on Data Cleaning and Handling Noise, was like a light bulb flicking on.
This chapter laid out different strategies for spotting bad or corrupted data points in a way that wasn’t overwhelming or too technical. The examples helped me see how the theory actually joins with what’s going on in real datasets, and understanding it kinda motivated me to mess around more with my assignments instead of just copying code from teammates.
That one chapter made a big difference in how much sense the rest of the data preprocessing steps made. It got me thinking more critically about why removing or fixing certain data matters and gave me little tricks like smoothing techniques and dealing with outliers that actually landed very well with how much work I have to do in class.
The textbook itself felt approachable, lots of diagrams sprinkled through which actually helped more than I expected, but some bits could’ve been a little less formal because the math notation felt a bit dense for my taste here and there, especially for someone who isn’t math-wizard level.
All in all, if you get the chance to pick this up free, and especially if noisy data is killing your vibe like it was mine, you’ll get more clarification than you expect. Definitely passing this onto other friends in the program. The insight from just that single chapter carried me pretty far already.
AF
Needed this for my Master of Science in Data Science studies and it came free which was cool. It showed up in like 2 days, no damage or anything. Haven't had time to read much but it feels pretty solid physically—nice quality pages and print. Not too heavy or anything, so easy to carry to class. The intro chapters I skimmed seemed okay but some parts felt a bit wordy, might not be the easiest to digest real quick. Anyway, good enough for getting started and I’ll see how it goes with homework in next weeks. Pretty happy with how fast it got here at least.
CP
So i've been using this intro book for my course CSCI 305: Data Mining and Knowledge Discovery for a couple weeks now since it came free, and while it’s pretty okay at explaining the basic stuff, some sections just drag a bit and get kinda dense, which can slow me down cuz the math sometimes feels like a wall. It’s got a simple layout though which helps a little. Not about to say it’s amazing or anything but it's alright if you want a free textbook that doesn’t confuse you completely. I’m hoping the later chapters get a bit more practical cause so far some parts feel like filler.




































