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What is the definition of data mining that the author mentions? 

After reviewing the case study this week by Krizanic (2020),write a paper:-

  1. What is the definition of data mining that the author mentions?  How is this different from our current understanding of data mining?
  2. What is the premise of the use case and findings?
  3. What type of tools are used in the data mining aspect of the use case and how are they used?
  4. Were the tools used appropriate for the use case?  Why or why not?
  5. Create the three clusters in Rapidminer or python and screenshot the result. 

 In an APA7 formatted and 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). 

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Big Data Mining Techniques and Implementation

CSCI316 (SIM) 202 2 Session 1 – Individual Assignment 2 CSCI316 – Big Data Mining Techniques and Implementation Individual Assignment 2 202 2 Session 1 (SIM) 15 Marks Deadline : Refer to the submission link on Moodle Two (2) tasks are included in this assignment. The specification of each task starts in a separate page. You must implement and run all your Python code in Jupyter Notebook. The deliverables include one Jupyter Notebook source file (with .ipybn extension) and one PDF document for each task. Note: To generate a PDF file for a notebook source file, you can either (i) use the Web browser’s PDF printing function, or (ii) click “File” on top of the notebook, choose “Download as” and then “PDF via LaTex”. All results of your implementation must be reproducible from your submitted Jupyter notebook source files. In addition, the submission must include all execution outputs as well as clear explanation of your implementation algorithms (e.g., in the Markdown format or as co mments in your Python codes). Submission must be done online by using the submission link associated with assignment 1 for this subject on MOODLE. The size limit for all submitted materials is 20MB. DO NOT submit a zip file. Submissions made after the du e time will be assessed as late submissions. Late submissions are counted in full day increments (i.e. 1 minute late counts as a 1 day late submission). There is a 25% penalty for each day after the due date including weekends. The submission site closes four days after the due date. No submission will be accepted after the submission site has closed. This is an individual assignment . Plagiarism of any part of the assignment will result in having 0 mark for the assignment and for all students involved. Marking guidelines Code: Your Python code will be assessed. The computers in the lab define the standard environment for code development and code execution. Note that the correctness, completeness, efficiency, and results of your executed code will be assessed. Thus, code t hat produces no useful outputs will receive zero marks. This also means that code that does not run on a computer in the lab would be awarded zero marks or code where none of the core functions produce correct results would be awarded zero marks. Present ation and explanation: The correctness, completeness and clearness of your answers will be assessed. CSCI316 (SIM) 202 2 Session 1 – Individual Assignment 2 Task 1 (7.5 marks) Data set : The Abalone Data Set (Source: https://archive.ics.uci.edu/ml/datasets/abalone ) Data set information These data consisted of 4 ,177 observations of 9 attributes , detailed as follows. Name / Data Type / Measurement Unit / Description —————————– Sex / nominal / — / M, F, and I (infant) Length / continuous / mm / Longest shell measurement Diameter / continuous / mm / perpendicular to length Height / continuous / mm / with meat in shell Whole weight / continuous / grams / whole abalone Shucked weight / continuous / grams / weight of meat Viscera weight / continuous / grams / gut weight (after bleeding) Shell weight / continuous / grams / after being dried Rings / integer / — / +1.5 gives the age in years Objective Implement a Naïve Bayesian classifier to predict the age of abalone in Python from scratch. Task requirements (1) Randomly separate the data into two subsets: ~70% for training and ~30% for test . (2) The Naïve Bayesian classifier must implements techniques to overcome the numerical underflows and zero counts . (3) No ML library can be used in this task. The implementation must be developed from scratch.

However, scientific computing libraries such as NumPy and SciP y are allowed. Deliverables • A Jupiter Notebook source file named _task 1.ipybn which contains your implementation source code in Python • A PDF document named _task 1.pdf which is generated from your Jupiter Notebook source file . CSCI316 (SIM) 202 2 Session 1 – Individual Assignment 2 Task 2 (7.5 marks) Data set : MAGIC Gamma Telescope Dataset (Source: https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope) Data set information The data are Monte -Carlo generated to simulate registration of high energy gamma particles in a ground – based atmospheric Cherenkov gamma telescope using the imaging technique. The dataset contains 19,020 records. Attribute information: 1. fLength: continuous # major axis of ellipse [mm] 2. fWidth: cont inuous # minor axis of ellipse [mm] 3. fSize: continuous # 10 -log of sum of content of all pixels [in #phot] 4. fConc: continuous # ratio of sum of two highest pixels over fSize [ratio] 5. fConc1: continuous # ratio of highest pixel over fSize [ratio] 6. f Asym: continuous # distance from highest pixel to center, projected onto major axis [mm] 7. fM3Long: continuous # 3rd root of third moment along major axis [mm] 8. fM3Trans: continuous # 3rd root of third moment along minor axis [mm] 9. fAlpha: continuous # angle of major axis with vector to origin [deg] 10. fDist: continuous # distance from origin to center of ellipse [mm] 11. class: g,h # gamma (signal), hadron (background) g = gamma (signal): 12332 h = hadron (background): 6688 Objective Develop an Artificial Neural Network (ANN) in TensorFlow/ Keras to predict the signal class . Requirements (1) Randomly separate the data into two subsets: ~70% for training and ~30% for test. (2) The training process includes a hyperparameter fine -tunning step. Define a grid including at least three hyperparameters: (a) the number of hidden layers, (b) the number of neurons in each layer, and (c) the regularization parameter s for L1 and L2. Each hyperparameter has at least two candidate values. All other parameters (e.g., activation functions and learning rates) are up to you. (Note. You can use Scikit -Learn for hyperparameter tuning , i.e., by using a Keras wrapper .) (3) Report the learning curve and test accuracy. Deliverables • A Jupiter Notebook source file named _task2.ipybn which contains your implementation source code in Python • A PDF document named _task2.pdf which is generated from your Jupiter Notebook source file

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Data mining functionalities

Given the 5 Data mining functionalities:

  1. Association: it’s the connection between two objects.
  2. Classification: if you’re a bank manager, you have to classify customers then only you can able to take decision whether I give the loan or not, if your customer is an expert rate you have to think twice before giving the loan if he’s Bahraini and from government job then it’s a guarantee that definitely he will get the money and we’ll able to get back the money.
  3. Clustering: grouping similar objects. For example, in this class I can able to group the people based on their gender (male/female), so then I can say boys go to as one group and girls you can go as another group so it means grouping the people. when we talk about clustering there’s one more technical term also included in clustering is outlier that means: the objects with which are not belongs to any groups. For example, if I can able to cluster the people based on gender (male/female) but very fractional people and those are not belonging to any other group both male or female, those people we can call them outliers we mean the trans genders.
  4. prediction: it’s about predicting the future based on our past experience.
    Given also data mining definition: the process of extracting or mining knowledge from the larger amount of data base.
    Q: what are the areas the data mining can be useful in it? I need 5 application areas and, in each area, you have to explain: 1) how data mining will be useful in it and 2) relate with the application areas the data mining functionalities and 3) to say in what way the functionality will be helpful for us in each application area.
    HINT?(for example let us say in the education sector, how the data mining is useful in the education sector? In education will be useful for me to predict (prediction) the students performance?so here as we can see we relate one functionality of data mining, also I have to mention in what basis I can predict the student performance? Based on test1,test2 results and from their assignments, attendance, behaviours…etc. so the data mining will be helpful for us to predict the students performance, why we have to predict it because I need to solve this problem that’s why I need this kind of predictions so by this way data mining will be helpful for me in the education sector)

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Explain the steps involved in data mining knowledge process

1. Explain the steps involved in data mining knowledge process

2. Data Mining Association Analysis: Basic Concepts and Algorithms Assignment

1) Explain the components and the use of the Mining Association Rules.

2) List and Explain the three “Frequent Itemset Generation Strategies”?

3) In Rule Generation, how do you efficiently generate rules from frequent itemsets?

4) In Support-Based Pruning: Most of the ___________________ algorithms use support measure to ______ rules and itemsets. (Fill in the blanks)

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The Need for Data Mining

ITS 632 Module One Essay Guidelines and Rubric

Topic: The Need for Data Mining

Overview: Over the last decade, advances in processing power and speed have enabled us to move beyond manual, tedious, and time- consuming data mining practices to quick, easy, and automated collection for data analysis. The more complex the data sets, the more potential there is to uncover relevant insights. Retailers, banks, manufacturers, telecommunications providers, and insurers are using data mining to discover relationships among everything from price optimization, promotions, and demographics to how the economy, risk, competition, and social media are affecting their business models, revenues, operations, and customer relationships.

Select one of the following industries and discuss the required topics:

 Medical  Banking/Finance  Marketing/Sales  Science and Engineering  Insurance  Retail

Guidelines for Submission: Using APA 6th edition style standards, submit a Word document that is 2-4 pages in length (excluding title page, references, and appendices) and include at least two credible scholarly references to support your findings. The UC Library is a good place to find these sources. Be sure to cite and reference your work using the APA guides and essay template that are located in the courseroom.

Include the following critical elements in your essay:

I. Use Case: Present an industry use case discuss the need for data mining in this particular industry. Describe how data mining can help organizations within the selected industry to retrieve the valuable information from the huge amount of data they collect. Explain how they make the data usable for analytical purposes, for business use, or for strategic planning purposes as a result of data mining processes.

II. Data Mining Challenges: Describe one challenge associated with data mining practices and processes within this industry. How can organizations overcome this challenge to gain a competitive advantage over their competitors?

III. Data Mining Techniques: Describe one data mining technique that would be useful for collecting the data required to support the industry and foster effective data analysis processes for decision-making (clustering, classification, pattern matching, association, regression, visualization, or meta rule-guided mining, for example). How will this technique help organizations in this industry overcome the challenge you mentioned?

Required elements:  Please ensure your paper complies APA 6th edition style guidelines. There is an essay template located under the Information link.  APA basics:

o Your essay should be typed, double-spaced on standard-sized paper (8.5″ x 11″) o Use 1″ margins on all sides, first line of all paragraphs is indented ½” from the margin o Use 12 pt. Times New Roman font o Include and introduction and conclusion (at least one paragraph)

 Follow the outline provided above and use section headers to improve the readability of your paper. If I cannot read and understand it, you will not earn credit for the content.

Critical Elements Proficient (100%) Needs Improvement (70%) Not Evident (0%) Value

Use Case

Presented an industry use case. Explains the need for data mining and how organizations make the data useful for analysis.

Industry use case lacks substantive details regarding the need for data mining or how organizations make the data useful for analysis.

Did not present a valid industry use case nor explain the need for data mining. No discussion of how organizations make the data useful for analysis with data mining.

30

Data Mining Challenges

Described one challenge associated with data mining practices and processes within the industry.

Explanation lacks substantive details regarding how the challenge inhibits the industry from achieving success or gaining a competitive advantage over its competitors.

Did not explain how the challenge inhibits the industry from achieving success or gaining a competitive advantage over its competitors.

30

Data Mining Techniques Described one data mining technique that would be useful for collecting the data required to

Did not describe one data mining technique that would be useful for collecting the data required to

Did not describe that would be useful for collecting the data required to support the industry and foster effective data analysis

30

support the industry and foster effective data analysis processes.

support the industry and foster effective data analysis processes.

processes. No explanation how of this technique will help organizations in this industry overcome data mining challenges.

Articulation of Response Submission has no major errors related to citations, grammar, spelling, syntax, or organization.

Submission has major errors related to citations, grammar, spelling, syntax, or organization that negatively impact readability and articulation of main ideas.

Submission has critical errors related to citations, grammar, spelling, syntax, or organization that prevent understanding of ideas.

10

EARNED TOTAL 100%

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Data mining in the story and why the data mining is important

Question:

Using search engines find two different recent articles involving data mining. Describe the role of “data mining” in the story and why the data mining is important your own words (at least 250 words for each URL). Be sure to cite your sources.

Here is an example of an article:

https://www.thedailystar.net/frontpage/new-police-unit-check-cyber-crime-1515997

Social media users to face stringent watch; police can detect users quickly

Requirements

APA

At least 250 words for each article

At least 4 credible scholarly references

No Plagiarism

Grading criteria

· Comprehension of Assignment (Addressed the question completely and thoroughly): 40 percent

· Clearly presents well-reasoned ideas and concepts: 40 percent

· Mechanics, punctuation, sentence structure, spelling, APA reference formatting: 20 percent

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Data Mining Solutions for Direct Marketing Campaign

Submission Deadline

Marks and Feedback

Before 10am on:

Tue 17/12/2019 Fri 20/12/2019 (if required an extension could be granted by Student Mitigation Team)

20 working days after deadline (L4, 5 and 7)

15 working days after deadline (L6)

10 working days after deadline (block delivery)

Unit title & code

CIS111-6: Intelligent Systems and Data Mining

Assignment number and title

Assignment 1: Data Mining Solutions for Direct Marketing Campaign

Assessment type

WR

Weighting of assessment

50%

Unit learning outcomes

Analyse a Data Mining technique capable of supporting practitioners to make reliable decisions which require predictive modelling, for example, in a Business scenario

Demonstrate results of using an efficient technique which is capable of finding a solution to a given predictive problem represented by a data set

Evaluate the accuracy of the technique in terms of differences between the predicted values and the given data

What am I required to do in this assignment?

1. Task

Students will develop a DM solution for saving cost of a direct marketing campaign by reducing false positive (wasted call) and false negative (missed customer) decisions. Working on this assignment, students can consider the following scenario.

A Bank has decided to save the cost of a direct marketing campaign based on phone calls offering a product to a client. A cost efficient solution is expected to support the campaign with predictions for a given client profile whether the client subscribes to the product or not. A startup company wants to develop an innovative DM technology which will be competitive on the market. The Manager will interview and hire Data Analysts. The team will analyse the existing technologies to design a DM solution winning the competition. A team Manager will choose the best solution for the market competition in terms of cost efficiency. The evaluation of the developed solutions will be made on the test data. The costs will be defined for both the false positive and false negative predictions.

Examples of cost-efficient DM solutions for direct marketing are provided on the UCI Machine Learning repository describing a Bank Marketing problem.

Students will apply for one of roles: (i) group manager, (ii) group member, or will work  individually. The group manager will arrange comparison and ranking of solutions designed in a group, and will have additional 5 points. Each student will run individual experiments to find an efficient solution and  describe differences in experimental results.

2. Method and Technology

To design a solution, students will use Data Mining techniques such as Decision Trees and Artificial Neural Networks. Examples of solutions will be provided in R Scripting using (i) a Cloud technology CoCalc or (ii) an advanced development suit RStudio free for students.

3. Data        

The Assignment 1 Bank Marketing data set is available as a csv file.

4. Report submission and report template

Each solution will be evaluated in terms of the costs of false decisions made on the validation data. Reports will be submitted via BREO.  Reports can be prepared with a template. BREO similarity level of reports must be < 20%.

Is there a size limit?

2000 words on average

What do I need to do to pass? (Threshold Expectations from UIF)

Apply Decision Tree technique to solve the Bank Marketing task presented by a set of customer profiles

Analyse problems which are required to be resolved in order to develop a solution providing a high prediction accuracy on a given data set.

How do I produce high quality work that merits a good grade?

Identify a set of parameters which are required to be adjusted within DM techniques in order to optimise the solution in terms of prediction accuracy

Explain how the parameters of a DM technique influence the prediction accuracy

Run experiments in order to verify the solution designed on the given data set

Analyse and compare the results of the experiments in a group and with the known from the literature.

How does assignment relate to what we are doing in scheduled sessions?

Data Mining techniques and use cases developed in R will be considered during  lectures and tutorials.

How will my assignment be marked?

Your assignment be marked according to the threshold expectations and the criteria on the following page.

You can use them to evaluate your own work and estimate your grade before you submit.

#

Weight, %

Lower 2nd – 50-59%

Upper 2nd – 60-69%

1st Class – 70%+

1

Analysis

(20)

Fair analysis of the basic approaches

Relatively good analysis of the relevant literature, mainly covering the state-of-art

Excellent analysis of the relevant literature, fully covering the state-of-art

2

Design

(50)

Fair design of a basic solution providing a reasonable performance within a single set of parameters

Design of a solution providing a fair performance in a series of experiments with different sets of parameters

Design of a solution providing a performance, competitive to known from the literature, in a series of experiments with different sets of parameters

3

Conclusion (30)

Fair conclusion on the experimental results obtained within a single set of parameters

Conclusion on and comparison of the experimental results obtained within two different sets of parameters

Conclusion on and comparison of the experimental results obtained within multiple sets of parameters, demonstrating a solution which provides a competitive performance