Assignment Introduction To Data Miningchapter 1this Week We Focus ✓ Solved

Assignment – Introduction to Data Mining CHAPTER - 1 This week we focus on the introductory chapter in which we review data mining and the key components of data mining. In an essay format answer the following questions: · What is knowledge discovery in databases (KDD)? · Review section 1.2 and review the various motivating challenges. Select one and note what it is and why it is a challenge. · Note how data mining integrates with the components of statistics and AL, ML, and Pattern Recognition. · Note the difference between predictive and descriptive tasks and the importance of each. In an APA7 formatted essay answer all questions above. There should be headings to each of the questions above as well.

Ensure there are at least two-peer reviewed sources to support your work. The paper should be at least two pages of content (this does not include the cover page or reference page). TextBook : 1) Data Mining: Concepts and Techniques Author: Jiawei Han, Jian Pei, Micheline Kamber Date: . 2) Tatti, V. (2012). Comparing apples and oranges: measuring differences between exploratory data mining results . Data Mining and Knowledge Discovery , 25 (2), 173–207.

Paper for above instructions

Data mining is a pivotal facet of modern data analytics, encompassing a suite of techniques employed to extract meaningful information from vast datasets. This essay aims to elucidate key elements from Chapter 1 of Jiawei Han, Jian Pei, and Micheline Kamber’s textbook "Data Mining: Concepts and Techniques" (2022), and address various components of data mining, such as Knowledge Discovery in Databases (KDD), challenges faced in this field, the intersection with statistical methods, artificial intelligence (AI), machine learning (ML), and pattern recognition, as well as the nuanced differences between predictive and descriptive tasks.

Knowledge Discovery in Databases (KDD)

Knowledge Discovery in Databases (KDD) refers to the comprehensive process of identifying valid, novel, useful, and understandable patterns in data (Han et al., 2022). This multi-step process encompasses several stages, beginning with data selection and preprocessing, followed by data transformation, data mining, pattern evaluation, and ultimately, knowledge presentation. KDD is more than mere data analysis; it encompasses all tasks involved in turning raw data into actionable knowledge.
The KDD process often begins with the identification of a specific problem domain, where data is gathered and prepared for analysis. The latter includes data cleaning to remove inconsistencies, data integration to combine sources, and data reduction, which is necessitated by the often overwhelming volume of data (Fayyad et al., 1996).
The primary distinction of KDD from straightforward data analysis lies in its origin as a structured process leading towards the extraction of knowledge. This structured framework is essential for domain experts aiming to make data-driven decisions. Therefore, in-depth understanding and implementation of KDD is crucial for organizations seeking to leverage their data repositories effectively.

Motivating Challenges in Data Mining: Data Quality

Among the various challenges highlighted in Section 1.2 of Han et al. (2022), data quality stands out as a significant hurdle. Data quality encompasses various dimensions, including accuracy, completeness, reliability, and consistency. Poor data quality can seriously affect the performance and reliability of any models built on this data.
For instance, if an organization attempts to analyze customer behavior based on erroneous or incomplete data, the subsequent insights gained from data mining may lead to misguided business strategies or misaligned marketing efforts. As reported by an estimate from the International Data Corporation, poor data quality costs U.S. businesses around