Question

1. Suppose that a large store has a transaction database that is distributed among four locations. Transactions in each component database have the same format, namely T_j: {i₁; …; i_m}, where T_jis a transaction identifier and i_k(1<=k <= m) is the identifier of an item purchased in the transaction. Propose an efficient algorithm to mine global association rules.

You may present your algorithm in the form of an outline. Your algorithm should not require shipping all of the data to one site and should not cause excessive network communication overhead.

1. [Contributed by Tao Cheng] Most frequent pattern mining algorithms consider only distinct items in a transaction. However, multiple occurrences of an item in the same shopping basket, such as four cakes and three jugs of milk, can be important in transaction data analysis. How can one mine frequent itemsets efficiently considering multiple occurrences of items? Propose modifications to the well-known algorithms, such as Apriori and FP-growth, to adapt to such a situation.

Answer 1

In the following problem we have large store has a transaction database that is distributed among four locations. distributed database management is describe by the database how to manage data from the different different location .let us we have the multiple transaction. but in each transaction of each component have the same format to getting data from the database.

the format should be like T_j: {i₁; …; i_m} tjis is the identifier. its means that these are the transaction are in the array.

theses transaction are like Tji1,Tji2,,Tji3,,Tji4,Tji5,,Tji6,,Tji7,.................................Tjim;

k is defining the item purchase. we can purchase the item between 1 to m. the item are between 1 to m.

before to proceeding we are understanding about the Association Rule for data mining from the database.

we are talking the another example for unnderstanding

takes as input a transaction database with utility information and a minimum utility threshold min_utility (a positive integer). Let's consider the following database consisting of 5 transactions (t1,t2...t5) and 7 items (1, 2, 3, 4, 5, 6, 7). This database is provided .

	Items	Transaction utility	Item utilities for this transaction
t1	3 5 1 2 4 6	30	1 3 5 10 6 5
t2	3 5 2 4	20	3 3 8 6
t3	3 1 4	8	1 5 2
t4	3 5 1 7	27	6 6 10 5
t5	3 5 2 7	11	2 3 4 2

• a set of items (the first column of the table),
• the sum of the utilities (e.g. profit) of these items in this transaction (the second column of the table),
• the utility of each item for this transaction (e.g. profit generated by this item for this transaction)(the third column of the table).

Note that the value in the second column for each line is the sum of the values in the third column.

What are real-life examples of such a database? There are several applications in real life. One application is a customer transaction database. Imagine that each transaction represents the items purchased by a customer. The first customer named "t1" bought items 3, 5, 1, 2, 4 and 6. The amount of money spent for each item is respectively 1 $, 3 $, 5 $, 10 $, 6 $ and 5 $. The total amount of money spent in this transaction is 1 + 3 + 5 + 10 + 6 + 5 = 30

output will be here is

itemsets	utility	support
{2 4}	30	40 % (2 transactions)
{2 5}	31	60 % (3 transactions)
{1 3 5}	31	40 % (2 transactions)
{2 3 4}	34	40 % (2 transactions)
{2 3 5}	37	60 % (3 transactions)
{2 4 5}	36	40 % (2 transactions)
{2 3 4 5}	40	40 % (2 transactions)
{1 2 3 4 5 6}	30	20 % (1 transactions)