Data Mining Introductory and Advanced Topics

Tailored for undergraduate courses in Computer Science & Information Technology/MCA, “Data Mining Introductory and Advanced Topics” is a comprehensive guide that takes students on a journey through the intricacies of data mining algorithms and concepts.

Database Perspective Throughout

Maintaining a database perspective throughout the book, the author provides a focused discussion on algorithms, data structures, data types, and the complexity of algorithms and space. This approach enables students to gain a holistic understanding of data mining, emphasizing its practical applications in real-world scenarios with large database components.

Key Features

– **Algorithm Emphasis:** The book places a strong emphasis on algorithms, ensuring that students not only understand the concepts but also the underlying processes that drive data mining.

– **Real-world Applications:** Demonstrating the relevance of data mining concepts, the book explores their application in real-world scenarios, particularly those involving large databases.

Book Details

Edition: 1st
Publisher: Pearson Education India
Publication Date: January 1, 2006
Language: English
Dimensions: 20.3 x 25.4 x 4.7 cm
Print Length: 328 pages

Why Choose “Data Mining Introductory and Advanced Topics”?

– **Curriculum Alignment:** Perfectly suited for undergraduate courses, the book aligns with the needs of students in Computer Science and Information Technology/MCA programs.

– **Practical Focus:** By maintaining a database perspective and emphasizing real-world applications, the book ensures that students are well-prepared for applying data mining concepts in professional settings.

– **Comprehensive Approach:** Covering a wide array of data mining topics and algorithms, this book offers a thorough exploration of the subject matter.

Equip yourself with the knowledge and skills essential for navigating the world of data mining. “Data Mining Introductory and Advanced Topics” is your gateway to understanding the intricacies of data mining and applying this knowledge to real-world scenarios.