Mining Partial Periodic Spatial Patterns in Temporal Databases

1. What is partial periodic spatial pattern mining?

Partial periodic spatial pattern mining aims to discover all interesting patterns in a geo-referenced temporal database that have periodic support no less than the user-specified minimum periodic support (minPS) constraint and the distance between two items is no less than maximum distance (maxDist). The minPS controls the minimum number of periodic occurrences of a pattern in a database.

2. What is the 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. What is the acceptable format of a temporal database in PAMI?

Each row in a temporal database must contain timestamp and items. A sample temporal database, say sampleTemporalDatabase.txt, is show below.

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 neighborhood database?

Neighborhood database contains the information regarding the items and their neighboring items.

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. What is the need for understand 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('sampleTemporalDatabase.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

6. What are the input parameters?

The input parameters to a partial periodic 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 ‘Item’ and ‘neighbours’

• minPS
  specified in

  • count (beween 0 to length of a database) or
  • [0, 1]

• maxIAT
  specified in

  • count (beween 0 to length of a database) or
  • [0, 1]

• seperator
  default seperator is ‘\t’ (tab space)

7. How to store the output of a partial periodic spatial pattern mining algorithm?

The patterns discovered by a partial periodic spatial pattern mining algorithm can be saved into a file or a data frame.

8. How to run the partial periodic spatial pattern mining algorithms in a terminal?

• Download the PAMI source code from github.
• Unzip the PAMI source code folder and enter into partial periodic spatial pattern folder.
• Enter into partialPeriodicSpatialPattern 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> <minPS> <maxIAT> <seperator>

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

10. How to execute a partial periodic spatial pattern mining algorithm in a Jupyter Notebook?

• 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.georeferencedPartialPeriodicPattern.basic.STEclat as alg

iFile = 'sampleTemporalDatabase.txt'  # specify the input transactional database <br>
nFile = 'sampleNeighbourFile.txt'  # specify the input transactional database <br>
minPS = 5  # specify the minSupvalue <br>
maxIAT = 3  # specify the minSupvalue <br>
seperator = ' '  # specify the seperator. Default seperator is tab space. <br>
oFile = 'partialSpatialPatterns.txt'  # specify the output file name<br>

obj = alg.STEclat(iFile, nFile, minPS, maxIAT, 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 Partial Periodic Patterns: 6
Total Memory in USS: 129249280
Total Memory in RSS 170835968
Total ExecutionTime in ms: 0.0015385150909423828

The partialSpatialPatterns.txt file contains the following patterns (format: pattern:periodicSupport):!cat partialSpatialPatterns.txt

!cat partialSpatialPatterns.txt

c	d: 7 
c	a: 5 
c: 8 
d: 7 
a: 6 
b: 6 

The dataframe containing the patterns is shown below:

df

	Patterns	periodicSupport
0	c\td\t	7
1	c\ta\t	5
2	c\t	8
3	d\t	7
4	a\t	6
5	b\t	6