QtyT40I10D100K

Donated on 10/20/2012

Since there is no numerical sequential data stream available in standard data sets, this data set is generated from the original T40I10D100K data set

Dataset Characteristics

Sequential

Subject Area

Business

Associated Tasks

Other

Feature Type

Integer

# Instances

3960456

# Features

-

Dataset Information

Additional Information

This data set is generated from the original T40I10D100K data set, to mine fuzzy sequential patterns over quantitative streams. While the original T40I10D100K is generated from the synthetic data generator described in “R. Agrawal, R. Srikant, Fast algorithms for mining association rules, 20th Intl. Conf. on Very Large Databases (VLDB’94), pp. 487-499. 1994”. The data set is a SQL Server 2008 database, which can be attached to a SQL Server Instance to use

Has Missing Values?

No

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
no
no
no
no

0 to 4 of 4

Additional Variable Information

CustomerID: the ID of the customer who has performed the transaction (randomly generated [1 100]) Time: the time that the transaction has been performed Transaction: the transaction which has been performed Quantity: the quantity value of each transaction (randomly generated [1 10])

Dataset Files

FileSize
QtyT40I10D100K.rar10.8 MB

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Download (10.8 MB)
Install the ucimlrepo package 
pip install ucimlrepo
Import the dataset into your code 
from ucimlrepo import fetch_ucirepo 
  
# fetch dataset 
qtyt40i10d100k = fetch_ucirepo(id=238) 
  
# data (as pandas dataframes) 
X = qtyt40i10d100k.data.features 
y = qtyt40i10d100k.data.targets 
  
# metadata 
print(qtyt40i10d100k.metadata) 
  
# variable information 
print(qtyt40i10d100k.variables)
View the full documentation
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Citation
Shakeri, O. & Pedram, M. (1994). QtyT40I10D100K [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5360W.
Style:

Creators

Omid Shakeri

Mir Pedram

DOI

License

This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.

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