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. 1994â€. 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 Name | Role | Type | Description | Units | Missing 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])
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
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)
Shakeri,Omid and Pedram,Mir. (2012). QtyT40I10D100K. UCI Machine Learning Repository. https://doi.org/10.24432/C5360W.
@misc{misc_qtyt40i10d100k_238,
author = {Shakeri,Omid and Pedram,Mir},
title = {{QtyT40I10D100K}},
year = {2012},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5360W}
}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.