Question

The Data Mining Process and manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns and trends in data include Bayes' theorem (circa 1700s) and regression analysis (circa 1800s). The proliferation, ubiquity and increasing power of computer technology has dramatically increased data collection, storage, and manipulation capabilities.

As data sets have grown and increased in complexity forming “Big Data” farms and structured Data Warehouses, "hands-on" data analysis has increasingly been enhanced with automated data processing and aided by other discoveries in computer science, such as neural networks, cluster analysis, genetic algorithms (circa 1950s), decision trees and decision rules (circa 1960s), and support vector machines (circa 1990s).

Data Mining is the process of applying these methods with the intention of uncovering hidden patterns and trends within large data warehouses. This helps to bridge the gap from applied statistics to artificial intelligence (AI), by exploiting the way data is stored and indexed in databases, thus producing the actual learning and execution of discovery algorithms, and allowing such methods to be applied to even larger data sets.

Discussion Topic #1:

Data Mining

Research the latest Privacy Issues with Data Mining and determine whether they are substantiated.

Also, research the most common mistakes and myths evolving around data mining.

Answer 1

Privacy Issues with the Data Mining :

1. Data mining can sometimes voilet privacy of the users, by gathering online as well as offline information to build a digital profile of a user.

2. Businesses such as insurance use data mining as a tool to get the customer information faster, which helps them to know the customer better so that it will be helpful while selling their products.

3. This is a new marketing tool to virtually gather the information without any overhead cost to the company. Recent survey conducted by Georgetown University states that, 92.8% websites collect the visitors personal data.

Mistakes and Myths Evolving Around Data Mining :

1. Usually obvious questions are asked instead of unusual questions by using analysis technique.

2. Sometimes they overreact to the results, where follow up studies is required to evaluate and analyse the information gathered.

3. Collecting small samples can sometimes mislead the results, which leads to inaccurate conclusions.

4. Data mining is supposed to be based on highly developd algorithms, whereas in reality only 10% of data mining process involves new and improved algorithms, other is related to setting business goals etc.