Concepts And Techniques Of Data Mining Computer Science Essay

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1a) Describe THOUGHTFULLY how to outline the basic  concepts and technique of Data Mining and how to apply them to real world problems. (At least one paragraph)

This is a description of some of the most common data mining algorithms in use today. We have divided this into two sections, each with a specific theme:

Ordinary Techniques: Statistics, Neighborhoods and Clustering

Future Techniques: Trees, Networks and Rules.

These two sections have been divided based when it is enough to be used for real world applications, especially for helping in the customization of customer relationship management systems. These techniques will be the ones that are used most of the time on existing business problems. Statistical techniques were used by the data and are used to discover patterns and build predictive models.

Data Mining is an analytic process maintained to explore data (large volumes of data - typically real world business) in search of consistent patterns and then to valuate the findings by applying the detected patterns to new subsets of data.

These patterns and trends will be defined as a model. Mining models are useful in case of real worlds applications such as:

Calculating sales

Contacting specific area of customers

Listing out the items sold

Finding the order that customers add products to a shopping cart

Building a mining model is a larger process that includes everything from creating queries about the data and creating a model to answer the queries, to deploying the model into model environment. This process can be defined by using the following six basic steps:

Defining the requirements

Data analysis

Data design

Developing Models

Validation of Models

Deploying and Updating Models

After completion of this class I was able to understand the typical patterns in data mining like decision trees, clusters and byes . we can outline the basic concepts and techniques of data Mining through the above concept.

1b) Describe THOUGHTFULLY how you learned to outline the basic the concepts and technique of Data Mining and how to apply them to real world problems .Give two (2) examples from your personal experience of each learning process. (At least one paragraph)

Ans : I have learned the basic concept and technique of Data Mining through the Example which is given by professor in classroom.

I am having a personal experience regarding the concepts of data mining. One of the professor from my university did a research on some genetic issues corresponds to pharmacy companies. we are having a long conversation about his research. and he told me that his entire research work is available on his website using some java application using a larger database.

He told to me that they not only storing the information on the flies but also doing "data mining". and added "which is very important these days to sustain". I told to him that i am learning this subject and he was interested to know the difference between "data mining" and statistics. There was no easy answer.

The reasons for the success of the techniques used in data mining, are the same reasons that statistical techniques are successful .the techniques are used in the same places for the same types of problems (prediction, classification discovery).

Another example is credit score reporting.

I 've learned the concept through this real world examples :A common marketing problem : Examine what people buy together to discover patterns.

1c) Discuss in at least two WELL-DEVELOPED paragraphs your ORIGINAL personal insights about concepts and technique of Data Mining and how to apply them to real world problems.

Ans : As I observed during my data mining course work about concepts and technique of Data Mining I can say that data mining uses statistical techniques, such as survival analysis , to determine the length of time for which a customer can be expected to stay with a company.

Based on the profile of customers, as indicated by demographics, price sensitivity and knowledge of alternative vendors, the length of their expected stay with a company can be estimated. So, this is a best real time example.

1d) Do you think this learning outcome is valuable or should it be changed? Justify your answer.

Ans : I think this outcome is very valuable in real world application .Because as technology improving user needs are also imcreasing. So this outcome I was able to understand the theory concept as well as practical aspect of data mining.

CSLO TWO:

Be able to know the basic concepts of data mining for internet application development

2a) Describe THOUGHTFULLY why it is important to Know the basic concepts of data mining for internet application development. (At least one paragraph)

Ans : internet is the powerful medium now a days.everyone is depending on the internet for daily needs.internet allowing to do every business or personal work.

At the same time the internet has large volumes of documents, like online library,music,movies etc. These data rich sites can easily use their stored, categorized data sets to build an automatic text classifier. The model can then be made available as a service where users can submit their documents and get back its position in the document taxonomy used at the site. Consider a new digital library or newspaper agency that wants to automatically categorize its submissions on a standard taxonomy. Instead of downloading the huge amounts of data on its site and spending money and effort in building a good automatic classifier, the agency might be willing to use the categorization service.

Anyone can develop large sets of data by creating data models to built knowledge servers.

Data mining allowing us to maintain huge sets of data online. Data mining becoming a good opportunity to internet server maintaining. I learned the concepts of data mining for internet application development through the useful information from the World-Wide Web and its usage patterns. So, it is important to know the basic concepts of data mining for internet application development.

By using the statistical and clustering techniques, data mining playing an important role in internet application development.

2b) Describe THOUGHTFULLY how you learned and be able to understand the basic concepts of data mining for internet application development.

Give two (2) examples from your personal experience of each learning process. (At least one paragraph)

Ans : I usually used to download and upload large volumes of data from the internet. In this process, I am aware how data mining used in internet application development. Storing large sets of data online is a required in present days. data mining techniques like clustering and statistics helps in this.

I am maintaining a blog in online to store books and useful information. this will helped me in understanding data mining concepts in internet application development. I have learned the basic concept of data mining for internet application development through the classical data mining and the World Wide Web.

I 've also learned how does data mining differ from classical data mining. So, after this class I was able to understand the basic concept of data mining fo e internet application development.

2c) Discuss in at least two WELL-DEVELOPED paragraphs your ORIGINAL personal insights about the basic concepts of data mining for internet application development.

Ans :

I am having an example over the data mining concepts for internet application development. That is IMDB[internet movie database],one of the well known website for movie ratings and information of world cinema.

In IMDB, movie ratings were calculated on the basis of the votes from the users. Means we can rate the movie by voting on the website. But we need to be a member on IMDB to rate the movie.

Millions of people uses IMDB for information. And millions of people express their review by voting or writing a review for a particular movie.

And it is a typical process for the website developers to analyze the rating of movie based on the votes from millions of people.

This can be possible by using the data mining concepts in IMDB application development. Statistical and clustering techniques were made use in order to consider each and every users interest.

We have to optimize large volumes of data to give a rating.

After completion of this class I was able to understand the basic concepts of data mining for internet application development. Because professor gave some real time examples in the classroom. So, after that it was really easy to understand.

2d) Do you think this learning outcome is valuable or should it be changed? Justify your answer.

Ans : This Outcome was very valuable for me because after completion of this class I understand the basic concepts of data mining for internet application development.

CSLO THREE:

Be able to know how to acquire, parse, filter, mine, represent, refine and interact with data.

3a) Describe THOUGHTFULLY why it is important to understand about how to acquire, parse, filter, mine, represent, refine and interact with data. (At least one paragraph)

Ans :

The process of understanding data starts from visualizing the data.

The main steps in visualization of data are as follows…

Acquire: Obtaining the data, whether from a file on a disk or a source over a network.

Parse: Providing a structure for the data's meaning, and order it into categories.

Filter: Removing all but the data of interest.

Mine: Applying methods from statistics or data mining as a way to discern patterns or

Place the data in mathematical context.

Represent: Choosing a basic visual model, such as a bar graph, list, or tree.

Refine: Improving the basic representation to make it clearer and more visually engaging.

Interact: Adding methods for manipulating the data or controlling what features are visible.

These are very important steps in the developing a model in data mining. Visualization plays an important role in data mining.

It is really important to understand the concept of acquire, parse, filter, mine, represent, refine and interact with data because it is becoming more and more important to use data mining techniques to use the vast amount of data in an optimized way.

But in order to draw optimized data, we have to perform the above steps in each case. It is not just sufficient to get the optimized data. First we need to visualize it to draw inferences and use it in an optimized way as well. This is where the seven steps of how to acquire, parse, filter, min, represent, refine and interact with data come in. So, it is important to understand each of these stages to be able to make a better use of the vast data available.

3b) Describe THOUGHTFULLY how you learned to acquire, parse, filter, mine, represent, refine and interact with data. Give two (2) examples from your personal experience of each learning process. (At least on paragraph)

Ans : I have learned these 7 concept using some practical examples and theory which is on professor's website. And it was really good material which will help me even in my future to get the job.

I am having a personal experience in this. that is zip code numbering system USA. most of the U.S. Postal Service uses these visualization techniques. The application is not an advanced one, but it provides a structure for how the process works.

It is easier to find a particular address by knowing the zipcode. the Zip code system Is designed on such a way that, we can easily find any location in usa.

We also discussed some of the examples from online sites. So, after that it was easy to understand for me.

3c) Discuss in at least two WELL-DEVELOPED paragraphs your ORIGINAL personal insights about acquire, parse, filter, mine, represent, refine and interact with data.

Ans : As per my personal insight view these 7 steps are very important as a part of data mining and visualization, because it is very important to optimally utilize the vast amounts of data required to be processed by our applications and also to provide people with the correct and up-to-date information. Each step, if properly understood and applied, can give a very high performance boost.

Data visualization becoming one of the most important data mining procedure now a days. These 7 steps were used in every process for data optimization.

3d) Do you think this learning outcome was valuable or should it be changed? Justify your answer.

Ans : I think this outcome is very useful for me as well as everybody because after completion of this outcome I was able to data mining algoritham and concept of data visualization.

CSLO FOUR:

Be familiar with concepts of Data Visualization. 

4a) Describe THOUGHTFULLY Why It Is Important to Know the concepts of Data Visualization. (At Least One Paragraph)

Ans : Data visualization is very important to know because Visualization is the graphical presentation of information. And data optimization only possible by visualization only. Data can be anything like Numeric, symbolic, Scalar, vector, or complex structure.So, it is important to know about the concept of data visualization.

Data Mining is an analytic process designed for maintaining data (large amounts of data - typically real world related) in search of consistent patterns or systematic relationships between variables, and then to validate the data models by applying the patterns to new data. The ultimate goal of data mining is prediction - and predictive data mining is the most common type of data mining and one that used in real world applications.

The main process of data mining consists of three stages:

(1) The requirements specification,

(2) Model design or pattern identification with validation, and

(3) Deployment

4b) Describe THOUGHTFULLY How You Learned concepts of Data Visualization. Give Two (2) Examples From Your Personal Experience Of Each Learning Process. (At Least One Paragraph)

Ans : I have learned the concept of data visualization concept after attending this class. And it was really interesting class. The slides from which professor tought it was really good material. And also I review all the slides for more information on data visualization.

I 've also learned dimension of data in this class.

4c) Discuss In At Least Two WELL-DEVELOPED Paragraphs Your ORIGINAL Personal Insights about concepts of Data Visualization.

Ans : As per my personal insight view I can say that data visualization Techniques for turning data into information by using the high capacity of the human brain to visually recognize patterns and trends. There are many specialized techniques designed to make particular kinds of visualization easy.

I've also learned some technique which makes a good visualization like Effectiveness, Accuracy, Efficincy, Aesthetics, and Adaptable.

4d) Do You Think This Learning Outcome Was Valuable Or Should It Be Changed? Justify Your Answer.

Ans : I think this learning outcome was very valuable for me. Because we tried some practicals in the classroom.

 CSLO FIVE:      

Be able to know how to approach data mining problem solving using a data mining tools and applications.

5a) Describe THOUGHTFULLY Why It Is Important to Know how to approach data mining problem solving using a data mining tools and applications . (At Least One Paragraph)

Ans :

Solving Data Mining Problems through Pattern Recognition is main task in overall project development. This is a multi-step method including defining the pattern recognition problem; collection, preparation, and preprocessing of data;

Choosing na algorithm and collecting algorithm parameters; and design, testing, and troubleshooting. Pattern classification, estimation, and modeling are identified by using one of the following algorithms: linear and logistic regression, unimodal Gaussian and Gaussian mixture, multilayered perceptron/backpropagation and radial basis function neural networks, K nearest neighbors and nearest cluster, and K means clustering. While some aspects of pattern recognition involve advanced mathematical principles.

5b) Describe THOUGHTFULLY How You Learned data mining problem solving using a data mining tools and applications. Give Two (2) Examples From Your Personal Experience Of Each Learning Process. (At Least One Paragraph)

Ans : I have learned the concept of data mining problem solving tools and application through practical example which is given by professor in the classroom.

For example, retail stores routinely usedata mining tools tolearn about purchasing habits of its customers.

Examples: Pressure,temp,blood test,cardiogram

Each test costs some anount

Data is unbalanced everytime

Data change time to time.

5c) Discuss In At Least Two WELL-DEVELOPED Paragraphs Your ORIGINAL Personal Insights about data mining problem solving using a data mining tools and applications.

Ans : As per my personal insight view there are a number of data mining software packages, including Intelligent Miner by IBM. However, for good data mining software combined with good statistical software.

Also there are some techniques for data mining like Artificial neural networks , Rule induction and data visualization.

5d) Do You Think This Learning Outcome Was Valuable Or Should It Be Changed? Justify Your Answer.

Ans : I think this outcome was very valuable for me because after completion of this class I 've understand how to approach data mining problem solving using a data mining tools and applications. Which is very important?

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