Construct FP tree for data given below, and find conditional pattern base and association rules.

To construct an FP-tree and find the conditional pattern base and association rules, we'll follow these steps:

1. Break Down the Problem

• Construct an FP-tree based on the given dataset.
• Extract conditional pattern bases from the FP-tree.
• Generate association rules from the conditional pattern bases.

2. Relevant Concepts

• FP-tree: A compressed representation of the dataset, focusing on frequent items.
• Conditional Pattern Base: The set of paths in the FP-tree that lead to a particular item.
• Association Rules: Rules that express a relationship between items, typically in the form of $A \rightarrow B$, where $A$ and $B$ are itemsets.

3. Analysis and Detail

Since you did not provide specific data to construct the FP-tree, here is a general process:

Example Data:

TID     Items
1       {A, B, D, E}
2       {B, C, E}
3       {A, B, C, E}
4       {B, E}
5       {A, B, C}

Step 1: Find Frequent Itemsets

1. Count the frequency of each item.
2. Set a minimum support threshold (for example, 2).

Step 2: Construct the FP-tree

1. Sort items in each transaction by descending frequency.
2. Insert transactions into the FP-tree.

Step 3: Extract Conditional Pattern Bases

1. For each item in the FP-tree, find the paths leading to that item.
2. Count support for these paths.

Step 4: Generate Association Rules

1. From the conditional pattern bases, use appropriate measures such as confidence and lift to establish rules.

4. Verify and Summarize

• Ensure that each step of item counting, tree construction, and rule generation respects the support threshold.
• Validate the rules by calculating their confidence.

Final Answer

Since the dataset was not specified, the process will need a specific dataset to display exact steps and results. Please provide the transaction data for a detailed construction or specify any particular dataset you are working with.