Spatio-Temporal Association Rule based Deep Annotation-free Clustering (STAR-DAC) for Unsupervised Person Re-identification

2021 ◽  
pp. 108287
Author(s):  
Sridhar Raj S ◽  
Munaga V.N.K Prasad ◽  
Ramadoss Balakrishnan
Keyword(s):  
Association Rule ◽  
Temporal Association ◽  
Rule Based ◽  
Temporal Association Rule ◽  
Spatio Temporal

scholarly journals Time-Aware CF and Temporal Association Rule-Based Personalized Hybrid Recommender System

2021 ◽  
Vol 33 (3) ◽  
pp. 19-34
Author(s):  
Dan Yang ◽  
Zheng Tie Nie ◽  
Fajun Yang
Keyword(s):  
Recommender System ◽  
Association Rule ◽  
Personalized Recommendation ◽  
Temporal Association ◽  
Recommendation Algorithm ◽  
Rule Based ◽  
Temporal Association Rule ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Time Aware

Most recommender systems usually combine several recommendation methods to enhance the recommendation accuracy. Collaborative filtering (CF) is a best-known personalized recommendation technique. While temporal association rule-based recommendation algorithm can discover users' latent interests with time-specific leveraging historical behavior data without domain knowledge. The concept-drifting and user interest-drifting are two key problems affecting the recommendation performance. Aiming at the above problems, a time-aware CF and temporal association rule-based personalized hybrid recommender system, TP-HR, is proposed. The proposed time-aware CF algorithm considers evolving features of users' historical feedback. And time-aware users' similar neighbors selecting measure and time-aware item rating prediction function are proposed to keep track of the dynamics of users' preferences. The proposed temporal association rule-based recommendation algorithm considers the time context of users' historical behaviors when mining effective temporal association rules. Experimental results on real datasets show the feasibility and performance improvement of the proposed hybrid recommender system compared to other baseline approaches.

Context Based Positive and Negative Spatio-Temporal Association Rule Mining

2013 ◽  
Vol 37 ◽  
pp. 261-273 ◽  
Author(s):  
Muhammad Shaheen ◽  
Muhammad Shahbaz ◽  
Aziz Guergachi
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Association ◽  
Rule Mining ◽  
Temporal Association Rule ◽  
Spatio Temporal

Fast Algorithms for Temporal Association Rules in a Large Database

2005 ◽  
Vol 277-279 ◽  
pp. 287-292 ◽  
Author(s):  
Lu Na Byon ◽  
Jeong Hye Han
Keyword(s):  
Electronic Commerce ◽  
Association Rules ◽  
Association Rule ◽  
Temporal Association ◽  
Large Database ◽  
Running Time ◽  
Smoothing Filter ◽  
Personalized Services ◽  
Temporal Association Rule ◽  
Transaction Database

As electronic commerce progresses, temporal association rules are developed by time to offer personalized services for customer’s interests. In this article, we propose a temporal association rule and its discovering algorithm with exponential smoothing filter in a large transaction database. Through experimental results, we confirmed that this is more precise and consumes a shorter running time than existing temporal association rules.

Ordering Policy and Inventory Classification Using Temporal Association Rule Mining

2018 ◽  
Vol 6 (1) ◽  
pp. 37-49 ◽  
Author(s):  
Reshu Agarwal
Keyword(s):  
Inventory Management ◽  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Association ◽  
Order Quantity ◽  
Rule Mining ◽  
Ordering Policy ◽  
Temporal Association Rule ◽  
Optimum Order ◽  
Frequent Items

A modified framework that applies temporal association rule mining to inventory management is proposed in this article. The ordering policy of frequent items is determined and inventory is classified based on loss rule. This helps inventory managers to determine optimum order quantity of frequent items together with the most profitable item in each time-span. An example is illustrated to validate the results.

Sign in / Sign up

Export Citation Format

Share Document