Mining Geo Referenced Periodic-Frequent Patterns in Temporal Databases

1. What is geo referenced periodic-frequent pattern mining?

Geo Referenced Periodic-Frequent pattern mining aims to discover all interesting patterns in a temporal database that have support no less than the user-specified minimum support (minSup) constraint, periodicity no greater than user-specified maximum periodicity (maxPer) constraint and distance between two items is no less than maximum distance (maxDist). The minSup controls the minimum number of transactions that a pattern must appear in a database and the maxPer controls the maximum time interval within which a pattern must reappear in the database.

2. What is a temporal database?

A temporal database is a collection of transactions at a particular timestamp, where each transaction contains a timestamp and a set of items.
A hypothetical temporal database containing the items a, b, c, d, e, f, and g as shown below

TS	Transactions
1	a b c g
2	b c d e
3	a b c d
4	a c d f
5	a b c d g
6	c d e f
7	a b c d
8	a e f
9	a b c d
10	b c d e

Note: Duplicate items must not exist in a transaction.

3. Acceptable format of temporal databases in PAMI

Each row in a temporal database must contain timestamp and items.

1 a b c g
2 b c d e
3 a b c d
4 a c d f
5 a b c d g
6 c d e f
7 a b c d
8 a e f
9 a b c d
10 b c d e

4. What is the spatial database?

Spatial database contain the spatial (neighbourhood) information of items. It contains the items and its nearset neighbours satisfying the maxDist constraint.

Items	neighbours
a	b, c, d
b	a, e, g
c	a, d
d	a, c
e	b, f
f	e, g
g	b, f

5. Understanding the statisctics of database

To understand about the database. The below code will give the detail about the transactional database.

Total number of transactions (Database size)
Total number of unique items in database
Minimum lenth of transaction that existed in database
Average length of all transactions that exists in database
Maximum length of transaction that existed in database
Minimum periodicity exists in database
Average periodicity exists in database
Maximum periodicity exists in database
Standard deviation of transaction length
Variance in transaction length
Sparsity of database

The below sample code prints the statistical details of a database.

import PAMI.extras.dbStats.TemporalDatabase as stats

obj = stats.TemporalDatabase('sampleInputFile.txt', ' ')
obj.run()
obj.printStats()

Database size : 10
Number of items : 7
Minimum Transaction Size : 3
Average Transaction Size : 4.0
Maximum Transaction Size : 5
Minimum period : 1
Average period : 1.0
Maximum period : 1
Standard Deviation Transaction Size : 0.4472135954999579
Variance : 0.2222222222222222
Sparsity : 0.42857142857142855

5. What are the input parameters?

The input parameters to a periodic frequent spatial pattern mining algorithm are:

Temporal database
Acceptable formats:
- String : E.g., ‘temporalDatabase.txt’
- URL : E.g., https://u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/transactional_T10I4D100K.csv
- DataFrame with the header titled ‘TS’ and ‘Transactions’
Neighbour database
Acceptable formats:
- String : E.g., ‘NeighbourDatabase.txt’
- URL : E.g., https://u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/transactional_T10I4D100K.csv
- DataFrame with the header titled ‘TS’ and ‘Transactions’
minSup
specified in
- count (beween 0 to length of a database) or
- [0, 1]
maxPer
specified in
- count (beween 0 to length of a database) or
- [0, 1]
seperator
default seperator is ‘\t’ (tab space)

6. How to store the output of a geo referenced periodic frequent pattern mining algorithm?

The patterns discovered by a geo referenced periodic frequent pattern mining algorithm can be saved into a file or a data frame.

7. How to run the geo referenced periodic frequent pattern mining algorithms in a terminal?

Download the PAMI source code from github.
Unzip the PAMI source code folder and enter into geo referenced periodic frequent pattern folder.
Enter into geoReferencedPeriodicFrequentPattern folder
Enter into a specific folder of your choice and execute the following command on terminal.

syntax: python3 algorithmName.py <path to the input file> <path to the output file> <path to the neighbour file> <minSup> <maxPer> <seperator>

Example: python3 GPFPMiner.py inputFile.txt outputFile.txt neighbourFile.txt 3 4 ' '

8. How to implement the GPFPMiner algorithm by importing PAMI package

Install the PAMI package from the PYPI repository by executing the following command: pip3 install PAMI
Run the below sample code by making necessary changes

import PAMI.geoReferencedPeriodicFrequentPattern.basic.GPFPMiner as alg

iFile = 'sampleInputFile.txt'  #specify the input temporal database <br>
nFile = 'sampleNeighbourFile.txt'  #specify the input neighbour database <br>
minSup = 5  #specify the minSupvalue <br>
maxPer = 3  #specify the maxPer value <br>
seperator = ' ' #specify the seperator. Default seperator is tab space. <br>
oFile = 'Patterns.txt'   #specify the output file name<br>

obj = alg.GPFPMiner(iFile, nFile, minSup, maxPer, seperator) #initialize the algorithm <br>
obj.mine()                       #start the mining process <br>
obj.save(oFile)               #store the patterns in file <br>
df = obj.getPatternsAsDataFrame()     #Get the patterns discovered into a dataframe <br>
obj.printResults()                      #Print the stats of mining process

Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm
Total number of Spatial Periodic-Frequent Patterns: 9
Total Memory in USS: 115994624
Total Memory in RSS 156839936
Total ExecutionTime in seconds: 0.0010027885437011719

The Patterns.txt file contains the following patterns (format: pattern:support:periodicity):!cat Patterns.txt

!cat Patterns.txt

d	c	a	: 5: 3 
c	d	: 8: 2 
c	a	: 6: 2 
c	: 9: 2 
d	a	: 5: 3 
d	: 8: 2 
b	a	: 5: 2 
b	: 7: 2 
a	: 7: 2 

The dataframe containing the patterns is shown below:

df

	Patterns	Support	Period
0	d\tc\ta\t	5	3
1	c\td\t	8	2
2	c\ta\t	6	2
3	c\t	9	2
4	d\ta\t	5	3
5	d\t	8	2
6	b\ta\t	5	2
7	b\t	7	2
8	a\t	7	2