User: ASHISH RAVINDNDRA SALVE

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Posts by ASHISH RAVINDNDRA SALVE

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Answer: A: Partition the given data into 4 bins using Equi-depth binning method and perform
... **divide the data into 4 equal-depth bins** bin 1:11,13,13,15,15,16 bin 2:9,20,20,20,21,21 bin3:22,23,24,30,40,45 bin4:,45,45,71,72,73,75 **smoothing by means** bin 1-13.83,13.83,13.83,13.83,13.83,13.83 bin 2-20.16,20.16,20.16,20.16,20.16 bin 3-30.67,30.67,30.67,30.67,30.67,30.67 bin 4-63.5 ...
written 8 days ago by ASHISH RAVINDNDRA SALVE0
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Answer: A: What is data preprocessing? Explain the different methods for the data cleansing
... In the data mining process the data need to be pre-processed first to make them quality data to acquire the quality analysis and information to make quality decision. Real world data are generally incomplete (lacking attribute values, lacking certain attributes of interest, or containing only aggr ...
written 8 days ago by ASHISH RAVINDNDRA SALVE0
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Answer: A: Explain box plot summary with example
... The box plot or box and whisker diagram is a standardized way of displaying the distribution of data based on the five number summary: minimum, first quartile, median, third quartile, and maximum. In the simplest box plot the central rectangle spans the first quartile to the third quartile (the inte ...
written 8 days ago by ASHISH RAVINDNDRA SALVE0
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Answer: A: Define data mining and Enumerate Five Example Application that can benefit by us
... **Data Mining:-** Data mining in the information system is like mining the earth. While mining finds out the hidden valuables in the earth, data mining not only finds out but also provides analysis of the hidden patterns of data in a data warehouse. Data mining aims at exploring knowledge from, dat ...
written 8 days ago by ASHISH RAVINDNDRA SALVE0
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Answer: A: Use the Apriori to algorithm to identify the frequent item-sets in the following
... Given:- | **TID** | **Items** | | --- | --- | | 01 | A, B, D, E, F | | 02 | B, C, E | | 03 | A, B, D, E | | 04 | A, B, D, E | | 05 | A, B, C, D, E, F | | 06 | B, C, D | | 07 | A, B, D, E | Solution:- Step 1: Generating Item set(support count = no of occurrences of item) | Item | Support | | --- ...
written 27 days ago by ASHISH RAVINDNDRA SALVE0
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Answer: A: For the same set of data points in question (Data: 11,13,13,15,15,16,19,20,20,20
... Given :- 11,13,13,15,15,16,19,20,20,20,21,21,22,23,24,30,40,45,45,45,71,72,73,75 ---------- A) (i) Mean:- Mean (x´) = (sum of all elements in data) / (no of elements in data) (x´) = ( 11+13+13+15+15+16+19+20+20+20+21+21+22+23+24+30+40+45+45+45+71+72+73+75) / (24) $= \frac{769}{24}$ ...
written 27 days ago by ASHISH RAVINDNDRA SALVE0
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Use the Apriori to algorithm to identify the frequent item-sets in the following database. Then extract the strong association rules from these sets.
... Min. Support = 30% Min. Confidence=75% |TID |Items| |-|-| |01|A, B, D, E, F| |02 |B, C, E| |03|A, B, D, E| |04|A, B, D, E| |05|A, B, C, D, E, F| |06|B, C, D| |07|A, B, D, E| ...
dmbi(26) written 5 weeks ago by ASHISH RAVINDNDRA SALVE0
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What is hierarchical clustering? Explain any two techniques for finding distance between the clusters in hierarchical clustering
... **Mumbai University > Information Technology > Sem6 > Data Mining and Business Intelligence** **Marks:** 10M ...
dmbi(26) written 5 weeks ago by ASHISH RAVINDNDRA SALVE0 • updated 5 weeks ago by abhishektiwari1712 ♦♦ 30
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Explain classifier evolution techniques.
... **Mumbai University > Information Technology > Sem6 > Data Mining and Business Intelligence** **Marks:** 10M ...
dmbi(26) written 5 weeks ago by ASHISH RAVINDNDRA SALVE0 • updated 5 weeks ago by abhishektiwari1712 ♦♦ 30
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Briefly explain Regression based Classifiers
... **Mumbai University > Information Technology > Sem6 > Data Mining and Business Intelligence** **Marks:** 10M ...
dmbi(26) written 5 weeks ago by ASHISH RAVINDNDRA SALVE0 • updated 5 weeks ago by abhishektiwari1712 ♦♦ 30

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Teacher 8 months ago, created an answer with at least 3 up-votes. For A: Explain BIRCH algorithm with example
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Popular Question 8 months ago, created a question with more than 1,000 views. For Explain BIRCH algorithm with example
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Epic Question 8 months ago, created a question with more than 10,000 views. For Clearly explain the working of the DB_SCAN algorithm using appropriate diagrams.
Epic Question 8 months ago, created a question with more than 10,000 views. For Explain BIRCH algorithm with example
Great Question 8 months ago, created a question with more than 5,000 views. For Explain multidimensional and multilevel association rules with an example.
Great Question 8 months ago, created a question with more than 5,000 views. For Explain different visualization techniques that can be used in data mining.
Great Question 8 months ago, created a question with more than 5,000 views. For Clearly explain the working of the DB_SCAN algorithm using appropriate diagrams.