Mining High-Utility Spatial Patterns in Utility Databases

PAMI is a Python library containing 100+ algorithms to discover useful patterns in various databases across multiple computing platforms. (Active)

Mining High-Utility Spatial Patterns in Utility Databases

1. What is High-Utility Spatial pattern mining?

High utility pattern mining aims to discover all the patterns with utility of pattern is no less than user-specified minimum utility threshold minutil and no distance between any two of its items should be no greater than user-specified maximum distance.

Reference: R. Uday Kiran, Koji Zettsu, Masashi Toyoda, Philippe Fournier-Viger, P. Krishna Reddy, and Masaru Kitsuregawa. 2019. Discovering Spatial High Utility Itemsets in Spatiotemporal Databases. In Proceedings of the 31st International Conference on Scientific and Statistical Database Management (SSDBM ‘19). Association for Computing Machinery, New York, NY, USA, 49–60. https://doi.org/10.1145/3335783.3335789

2. What is a utility database?

A utility database is a collection of transaction, where each transaction contains a set of items and a positive integer called internal utility respectively. And each unique item in database is also associated with another positive number called external utility for each transaction.
A hypothetical utility database with items a, b, c, d, e, f and g and its internal utility is shown below at right side and items with its external utilities for each transaction is presented at left side.

Transactions external utilities
(a,2) (b,3) (c,1) (g,1) 5 4 3 2
(b,3) (c,2) (d,3) (e,2) 5 2 9 3
(a,2) (b,1) (c,3) (d,4) 2 3 5 6
(a,3) (c,2) (d,1) (f,2) 1 3 4 6
(a,3) (b,1) (c,2) (d,1) (g,2) 2 5 3 6 1
(c,2) (d,2) (e,3) (f,1) 2 3 4 5
(a,2) (b,1) (c,1) (d,2) 5 4 3 2
(a,1) (e,2) (f,2) 4 8 3
(a,2) (b,2) (c,4) (d,2) 7 4 9 8
(b,3) (c,2) (d,2) (e,2) 5 9 10 24

Note: Duplicate items must not exist in a transaction.

3. What is the acceptable format of a utility database in PAMI?

Each row in a utility database must contain only items, total sum of utilties and utility values. A sample transactional database, say sampleInputFile.txt, is provided below.

A sample utility database, say sampleUtilitySpatial.txt, is provided below.

a b c g:7:2 3 1 1:5 4 3 2
b c d e:10:3 2 3 2:5 2 9 3
a b c d:10:2 1 3 4:2 3 5 6
a c d f:7:3 2 1 2:1 3 4 6
a b c d g:9:3 1 2 1 2:2 5 3 6 1
c d e f:8:2 2 3 1:2 3 4 5
a b c d:6:2 1 1 2:5 4 3 2
a e f:5:1 2 2:4 8 3
a b c d:10:2 2 4 2:7 4 9 8
b c d e:9:3 2 2 2:5 9 10 24

4. What is a neighbourhood database?

A neighborhood database contains items and their neighbors. An item x is said to be a neighbor of y if the distance between x and y is no more than the user-specified maximum distance threshold value.
A hypothetical spatial database containing items a, b, c, d, e, f and g and neighbours respectively is shown below.

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

The methodology to create a neighborhood database file from a given geo-referenced database has been described in the manual creatingNeighborhoodFile.pdf

5. What is the acceptable format of a neighborhood database?

The format of the neighborhood database is similar to that of a transactional database. That is, each transaction must contain a set of items. In a transaction, the first item represents the key item, while the remaining items represent the neighbors of the first item.

A sample neighborhood file, say sampleNeighbourFile.txt, is provided below:

a b c d
b a e g
c a d
d a c
e b f
f e g
g b f

6. What is the need for understanding the statistics of a utility database?

The performace of a pattern mining algorithm primarily depends on the satistical nature of a database. Thus, it is important to know the following details of a database:

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

import PAMI.extras.dbStats.UtilityDatabase as stats

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

7. What are the input parameters to a high utility spatial pattern mining algorithm?

Algorithms to mine the high utility spatial patterns require utility database, neighborhood database, and a user-specified minUtil constraint. Please note that maxDist constraint has been used in prior to create a neighborhood database file.

8.How to store the output of a frequent pattern mining algorithm?

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

9. How to execute a high utility spatial pattern mining algorithm in a terminal?

Example: python3 HDSHUIM.py inputFile.txt outputFile.txt neighbourFile.txt $20$   ' '

10. How to execute a high utility spatial pattern mining algorithm in a Jupyter Notebook?

import PAMI.highUtilitySpatialPattern.basic.HDSHUIM as alg 

iFile = 'sampleUtilitySpatial.txt'  #specify the input utility database
nFile = 'sampleNeighbourFile.txt'      #specify the neighbour file of database 
minUtil = 20                        #specify the minUtil value 
seperator = ' ' #specify the seperator. Default seperator is tab space. 
oFile = 'utilityPatterns.txt'   #specify the output file name

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

7
Total number of Spatial High Utility Patterns: 4
Total Memory in USS: 114114560
Total Memory in RSS 152379392
Total ExecutionTime in seconds: 0.0016164779663085938

!cat utilityPatterns.txt
#The format of the file is pattern:support

a	d:22 
a	d	c:34 
a	c:27 
d	c:35 

df
# zthe dataframe containing the patterns is shown below.
Patterns Support
0 a d 22
1 a d c 34
2 a c 27
3 d c 35