학술논문

Utility-based Frequent Itemsets in Data Streams using Sliding Window
Document Type
Conference
Source
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Computing, Communication, and Intelligent Systems (ICCCIS),2021 International Conference on. :108-112 Feb, 2021
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Economics
Consumer behavior
Itemsets
Memory management
Data structures
Data models
Task analysis
frequent itemsets
data stream
sliding window
utility
data mining
consumer behaviour
rationality
behavioural Economics
Language
Abstract
There is a need for increased use of Market Basket Analysis in Economics to estimate consumer behaviour and demand function more realistic especially in a data streaming environment, which is a challenging task. A sliding window contains the latest fixed number of elements of the data stream. The algorithm FIMIU, proposed in this paper, replaces the itemsets in the sliding window by pointers to a single copy of the itemset, thereby creating more space for new itemsets in it which allows the user to analyze a bigger part of the data stream at a time. Experiments have shown that the proposed algorithm is memory efficient, however requires a bit extra time.