Chapter: Data Mining and Privacy

Quote:

If the courts will not protect private data from being associated with each other and forming new information about an individual, there is a long-standing set of methodologies, based on cryptographic protocols, which can provide such protection. Devised by computer scientist David Chaum, these techniques prevent the dossier society in which computers could be used to infer individuals’ life styles, habits, whereabouts, and associations from data collected in ordinary consumer transactions can have a chilling effect causing the people to alter their observable activities. While answering the need for organizations to devise more persuasive, efficient and interlinked computerized record keeping systems so that everything from consumer credit social services is not abused. Unfortunately, there is no real incentive for organizations to implement these rather simple methodologies, despite consumer concern with data collection and mining. Thus, as long as the law silent on the subject, it appears that existing non-intrusive technological means of solving this problem will remain merely a unit in various advanced courses in the computer science curriculum.

What I expect to learn:

To be aware how data mining is used and how its privacy can be enforced.

Review:                    

I know, the first I thought when I first heard “data mining” from one of my major subjects is mining for data and how redundant is that. But no matter how redundant or non-sense it is, it is the true meaning of “data mining”.

It is the actual straining or filtering of data from a data warehouse. Remember when your favourite cereal is on the table and the only pieces you prefer are the pink and yellow ones? That is exactly what exactly how data mining works or at least not in the literal way. They filter information and get the requested information from the data warehouse necessary to the company. It may be that obvious that trying to create something from scratch is a waste of time so the best way is to reuse and reuse the existing resources within the physical bounds of the company or outside. Extracting the necessary or the desired information that will be utilized and will eventually be analyzed for the use of the business is a crucial process. It will also allow other possible resources to be utilized and not be wasted. One thing I learned from my production management course, “Never allow something to be a waste.” I am not saying that we should be cheap about it, I am just saying that it may not be obvious but saving up and thinking of ways to use “wastes” will really help the company in a whole new different level. Discover all the possibilities and you will be shocked.

What I learned:

·         Knowledge Discovery and Data Mining

·         The issue

·         Analysis of the issue

·         Issues in the first case as well as the second case

·         Categories in pre-existing data

·         Cluster data by mapping

Integrative Questions:            

1.    What do you mean by knowledge discovery?

2.    Define data mining.

3.    Provide at least three (3) examples of the usage of data mining.

4.    How do you analyze an issue?

5.    What are the categories of pre-existing data?