(Data Mining) proove backpropagation algorithm.
Solution:
This question is for frequent pattern mining algorithm Apriori and closed pattern mining algorithm like CLOSET. Implement Apriori algorithm to mine frequent pattern from a transaction dataset Implement an algorithm to mine closed frequent pattern from the same dataset. You can either write a code to extract closed patterns from the result that you got in Part 1 or code CLOSET. Input Format The input dataset is a transaction dataset. The first line of the input corresponds to the minimum...
Data Mining: Explain why decision tree algorithm based on impurity measures such as entropy and Gini index tends to favor attributes with larger number of distinct values. How would you overcome this problem?
Question in Data mining :
Apply Apriori algorithm on the grocery store example with support threshold s = 33.34% and confidence threshold c = 60%, where H, B, K, C and P are different items purchased by customers. Show all final frequent itemsets. Specify the association rules that are generated. Show final association rules sorted by confidence. Represent the transactions as graph.
1.Explain how the nearest neighbor algorithm works. 2. Explain how the backpropagation algorithm works.
You are a data mining consultant hired by your organization to implement a data mining process. What challenges does your organization face in ensuring that the data mining models are receiving clean data?
Derive the Backpropagation algorithm (only for the output nodes of a multi-layer perceptron) if the activation function is: phi(v) = e^-(v/sigma)^2 where sigma is a parameter to be learned for each neuron? Simplify the equations as much as possible.
Identify a commercial data mining software product (e.g., SAS) and an open-source data mining product (e.g., R). Based on objective reviews of each (e.g., Gartner), compare and contrast the two products.
Data Mining using R question help: Why are the attribute ranges so important when doing linear regression data mining?
Explain OLAP and data mining processes. Include the following in your discussion: The functions of OLAP and data mining How OLAP and data mining complement one another How OLAP and data mining exist independently An example of when each system would be used Tools available to support each phase of these processes