Читать книгу Data Mining and Machine Learning Applications - Группа авторов - Страница 12

Santosh R. Durugkar1, Rohit Raja2, Kapil Kumar Nagwanshi3* and Sandeep Kumar4

¹ Amity University Rajasthan, Jaipur, India

² IT Department, GGV Bilaspur Central University, Bilaspur, India

³ ASET, Amity University Rajasthan, Jaipur, India

⁴ Computer Science and Engineering Department, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andra Pradesh, India

Abstract

Data mining, as its name suggests “mining”, is nothing but extracting the desired, meaningful exact information from the datasets. Its methods and algorithms help researchers and students develop the numerous applications to be used by the end-users. Its presence in the healthcare industry, marketing, scientific applications, etc., enables the end-users to extract the meaningful required information from the collection. In the initial section, we discuss KDD—knowledge discovery in the database with its different phases like data cleaning, data integration, data selection and transformation, representation. In this chapter, we give a brief introduction to data mining. Comparative discussion about classification and clustering helps the end-user to distinguish these techniques. We also discuss its applications, algorithms, etc. An introduction to a basic clustering algorithm, K-means clustering, hierarchical clustering, fuzzy clustering, and density-based clustering, will help the end-user to select a specific algorithm as per the application. In the last section of this chapter, we introduce various data mining tools like Python, Rapid Miner, and KNIME, etc., to the user to extract the required information.

Keywords: Data mining, KDD, clustering, classification, Python, KNIME