TubeS
- PAMI.uncertainFrequentPattern.basic.TubeS.Second(transaction, i)[source]
To calculate the second probability of a node in transaction :param transaction: transaction in a database :param i: index of item in transaction :return: second probability of a node
- class PAMI.uncertainFrequentPattern.basic.TubeS.TubeS(iFile, minSup, sep='\t')[source]
Bases:
_frequentPatterns- Description:
TubeS is one of the fastest algorithm to discover frequent patterns in a uncertain transactional database.
- Reference:
Carson Kai-Sang Leung and Richard Kyle MacKinnon. 2014. Fast Algorithms for Frequent Itemset Mining from Uncertain Data. In Proceedings of the 2014 IEEE International Conference on Data Mining (ICDM ‘14). IEEE Computer Society, USA, 893–898. https://doi.org/10.1109/ICDM.2014.146
- Attributes:
- iFilefile
Name of the Input file or path of the input file
- oFilefile
Name of the output file or path of the output file
- minSupfloat or int or str
The user can specify minSup either in count or proportion of database size. If the program detects the data type of minSup is integer, then it treats minSup is expressed in count. Otherwise, it will be treated as float. Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float
- sepstr
This variable is used to distinguish items from one another in a transaction. The default seperator is tab space or . However, the users can override their default separator.
- memoryUSSfloat
To store the total amount of USS memory consumed by the program
- memoryRSSfloat
To store the total amount of RSS memory consumed by the program
- startTimefloat
To record the start time of the mining process
- endTimefloat
To record the completion time of the mining process
- Databaselist
To store the transactions of a database in list
- mapSupportDictionary
To maintain the information of item and their frequency
- lnoint
To represent the total no of transaction
- treeclass
To represents the Tree class
- itemSetCountint
To represents the total no of patterns
- finalPatternsdict
To store the complete patterns
- Methods:
- mine()
Mining process will start from here
- getPatterns()
Complete set of patterns will be retrieved with this function
- save(oFile)
Complete set of frequent patterns will be loaded in to a output file
- getPatternsAsDataFrame()
Complete set of frequent patterns will be loaded in to a dataframe
- getMemoryUSS()
Total amount of USS memory consumed by the mining process will be retrieved from this function
- getMemoryRSS()
Total amount of RSS memory consumed by the mining process will be retrieved from this function
- getRuntime()
Total amount of runtime taken by the mining process will be retrieved from this function
- creatingItemSets(fileName)
Scans the dataset and stores in a list format
- frequentOneItem()
Extracts the one-length frequent patterns from database
- updateTransactions()
Update the transactions by removing non-frequent items and sort the Database by item decreased support
- buildTree()
After updating the Database, remaining items will be added into the tree by setting root node as null
- convert()
to convert the user specified value
Methods to execute code on terminal
- Format:
>>> python3 TubeS.py <inputFile> <outputFile> <minSup>
- Example:
>>> python3 TubeS.py sampleTDB.txt patterns.txt 3
Note
minSup will be considered in support count or frequency
Importing this algorithm into a python program
Credits:
The complete program was written by P.Likhitha under the supervision of Professor Rage Uday Kiran.
- getMemoryRSS()[source]
Total amount of RSS memory consumed by the mining process will be retrieved from this function :return: returning RSS memory consumed by the mining process :rtype: float
- getMemoryUSS()[source]
Total amount of USS memory consumed by the mining process will be retrieved from this function :return: returning USS memory consumed by the mining process :rtype: float
- getPatterns()[source]
Function to send the set of frequent patterns after completion of the mining process :return: returning frequent patterns :rtype: dict
- getPatternsAsDataFrame()[source]
Storing final frequent patterns in a dataframe :return: returning frequent patterns in a dataframe :rtype: pd.DataFrame
- getRuntime()[source]
Calculating the total amount of runtime taken by the mining process :return: returning total amount of runtime taken by the mining process :rtype: float
- save(outFile)[source]
Complete set of frequent patterns will be loaded in to an output file :param outFile: name of the output file :type outFile: file