On Mining Movement Pattern from Mobile Users

2007 ◽  
Vol 3 (1) ◽  
pp. 69-86 ◽  
Author(s):  
David Taniar ◽  
John Goh
Keyword(s):  
Pattern Mining ◽  
Movement Pattern ◽  
Mobile User ◽  
Mobile Users ◽  
Data Mining Algorithm ◽  
Movement Data ◽  
Mining Algorithm ◽  
The Individual ◽  
Base Location

In the era in which activities performed by mobile users are tracked through various sensing mechanisms, the movement data collected through these sensors is submitted into a data mining algorithm in order to determine the movement pattern. The movement pattern refers to the pattern that mobile users generally take to move from one base location to another base location through multiple intermediate locations. This paper provides a proposal and case study on how the movement pattern can be extracted from mobile users through transforming the user movement database to the location movement database and subsequently transferred to an algorithm Apriori-like movement pattern (AMP) and movement tree (M-tree). The result is a list of sequences in which mobile users frequently go through that which satisfies min-support and min-confidence. The result of this movement pattern mining exercise opens up a new future for the prediction of the movement for the individual mobile user.

scholarly journals Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan

2018 ◽  
Author(s):  
Xinning Zhu ◽  
Tianyue Sun ◽  
Hao Yuan ◽  
Zheng Hu ◽  
Jiansong Miao
Keyword(s):  
Pattern Mining ◽  
Historical Data ◽  
Movement Pattern ◽  
Phone Call ◽  
Specific Group ◽  
Group Movement ◽  
Trajectory Similarity ◽  
Group Members ◽  
Call Detail Records

Identifying group movement patterns of crowds and understanding group behaviors is valuable for urban planners, especially when the groups are special such as tourist groups. In this paper, we present a framework to discover tourist groups and investigate the tourist behaviors using mobile phone call detail records (CDRs). Unlike GPS data, CDRs are relatively poor in spatial resolution with low sampling rates, which makes it a big challenge to identify group members from thousands of tourists. Moreover, since touristic trips are not on a regular basis, no historical data of the specific group can be used to reduce the uncertainty of trajectories. To address such challenges, we propose a method called group movement pattern mining based on similarity (GMPMS) to discover tourist groups. To avoid large amounts of trajectory similarity measurements, snapshots of the trajectories are firstly generated to extract candidate groups containing co-occurring tourists. Then, considering that different groups may follow the same itineraries, additional traveling behavioral features are defined to identify the group members. Finally, with Hainan province as an example, we provide a number of interesting insights of travel behaviors of group tours as well as individual tours, which will be helpful for tourism planning and management.

Improvement of Spatial and Temporal Co-Occurrence Pattern for Time Aggregate Graph

2014 ◽  
Vol 998-999 ◽  
pp. 1352-1356
Author(s):  
Jian Ying He ◽  
Qing Song Tang
Keyword(s):  
Spatial Data ◽  
Pattern Mining ◽  
Pattern Analysis ◽  
Large Data ◽  
Data Mining Algorithm ◽  
Efficiency Improvement ◽  
Mining Algorithm ◽  
Data Environment ◽  
Occurrence Pattern ◽  
Improved Model

It is very important to build model by data mining algorithm of spatial data under large data environment using spatial and temporal co-occurrence pattern, analysis is conducted in view of existing Time Aggregate Graph spatial and temporal co-occurrence pattern, mining efficiency improvement is obtained, an improved spatial and temporal co-occurrence pattern is proposed, the improved model is verified to increase efficiency by instances.

scholarly journals Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan

2019 ◽  
Vol 8 (2) ◽  
pp. 74 ◽  
Author(s):  
Xinning Zhu ◽  
Tianyue Sun ◽  
Hao Yuan ◽  
Zheng Hu ◽  
Jiansong Miao
Keyword(s):  
Pattern Mining ◽  
Historical Data ◽  
Movement Pattern ◽  
Phone Call ◽  
Specific Group ◽  
Group Movement ◽  
Trajectory Similarity ◽  
Group Members ◽  
Call Detail Records

Identifying group movement patterns of crowds and understanding group behaviors are valuable for urban planners, especially when the groups are special such as tourist groups. In this paper, we present a framework to discover tourist groups and investigate the tourist behaviors using mobile phone call detail records (CDRs). Unlike GPS data, CDRs are relatively poor in spatial resolution with low sampling rates, which makes it a big challenge to identify group members from thousands of tourists. Moreover, since touristic trips are not on a regular basis, no historical data of the specific group can be used to reduce the uncertainty of trajectories. To address such challenges, we propose a method called group movement pattern mining based on similarity (GMPMS) to discover tourist groups. To avoid large amounts of trajectory similarity measurements, snapshots of the trajectories are firstly generated to extract candidate groups containing co-occurring tourists. Then, considering that different groups may follow the same itineraries, additional traveling behavioral features are defined to identify the group members. Finally, with Hainan province as an example, we provide a number of interesting insights of travel behaviors of group tours as well as individual tours, which will be helpful for tourism planning and management.

Data Mining for Subassembly Selection

2004 ◽  
Vol 126 (3) ◽  
pp. 627-631 ◽  
Author(s):  
Bruno Agard ◽  
Andrew Kusiak
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Data Mining Algorithm ◽  
Optimization Framework ◽  
Manufacturing Efficiency ◽  
Mining Algorithm ◽  
Design And Manufacturing ◽  
Timely Delivery ◽  
Selection Of

The paper presents a model and an algorithm for selection of subassemblies based on the analysis of prior orders received from the customers. The parameters of this model are generated using association rules extracted by a data mining algorithm. The extracted knowledge is applied to construct a model for selection of subassemblies for timely delivery from the suppliers to the contractor. The proposed knowledge discovery and optimization framework integrates the concepts from product design and manufacturing efficiency. The ideas introduced in the paper are illustrated with an example and an automotive case study.

scholarly journals Data Mining Algorithm for Demand Forecast Analysis on Flash Sales Platform

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mingyang Zhang ◽  
Yixin Wang ◽  
Zhiguo Wu
Keyword(s):  
Decision Making ◽  
Autoregressive Model ◽  
Evaluation Index ◽  
Data Mining Algorithm ◽  
Demand Forecast ◽  
Customer Reviews ◽  
Product Demand ◽  
Emotional Factors ◽  
Mining Algorithm

With the development of the digital economy, the emerging marketing strategy of the e-commerce flash sales has been changing the traditional purchasing habits of customers. This imposes new decision-making challenges for companies involved in flash sales. It is important for companies to build the accurate product demand forecast analysis focusing on the characteristics of the flash sales and customer behaviors. In this paper, VIPS (Weipinhui, a Chinese e-commerce platform) is taken as a case study with the key focus on how sentiment factors in customer reviews affect product demand in flash sale platforms. The paper adopts two sentiment analysis methods based on emotional dictionaries. The method with a higher evaluation index is adopted to integrate the emotional factors into the autoregressive model for product demand and assessment. The experiments prove that the autoregressive model for integrating the sentiment factors demonstrates better forecasting performances than the models without sentiment factors. The experiments further confirm that when product demand for the previous two weeks and customer review sentiment factors in the previous week are taken into consideration, demand forecast effects are most accurate.

The Development of Language-Training Programs for Postrubella Hearing-Impaired Children

1973 ◽  
Vol 38 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Linda Lynch ◽  
Annette Tobin
Keyword(s):  
Hearing Impaired ◽  
Treatment Programs ◽  
Individual Treatment ◽  
Target Behaviors ◽  
Systematic Fashion ◽  
Development Of Language ◽  
The Individual ◽  
Impaired Children ◽  
Hearing Impaired Children

This paper presents the procedures developed and used in the individual treatment programs for a group of preschool, postrubella, hearing-impaired children. A case study illustrates the systematic fashion in which the clinician plans programs for each child on the basis of the child’s progress at any given time during the program. The clinician’s decisions are discussed relevant to (1) the choice of a mode(s) for the child and the teacher, (2) the basis for selecting specific target behaviors, (3) the progress of each program, and (4) the implications for future programming.

‘A Routh O’ Auld Nick-Nackets’ – the antiquarian collection of John Rae

2015 ◽  
Vol 36-37 (1) ◽  
pp. 163-183
Author(s):  
Paul Taylor
Keyword(s):  
United States ◽  
Nineteenth Century ◽  
Social Context ◽  
Moral Education ◽  
The United States ◽  
Representative Case ◽  
National Museums ◽  
The Individual ◽  
The University

John Rae, a Scottish antiquarian collector and spirit merchant, played a highly prominent role in the local natural history societies and exhibitions of nineteenth-century Aberdeen. While he modestly described his collection of archaeological lithics and other artefacts, principally drawn from Aberdeenshire but including some items from as far afield as the United States, as a mere ‘routh o’ auld nick-nackets' (abundance of old knick-knacks), a contemporary singled it out as ‘the best known in private hands' (Daily Free Press 4/5/91). After Rae's death, Glasgow Museums, National Museums Scotland, the University of Aberdeen Museum and the Pitt Rivers Museum in Oxford, as well as numerous individual private collectors, purchased items from the collection. Making use of historical and archive materials to explore the individual biography of Rae and his collection, this article examines how Rae's collecting and other antiquarian activities represent and mirror wider developments in both the ‘amateur’ antiquarianism carried out by Rae and his fellow collectors for reasons of self-improvement and moral education, and the ‘professional’ antiquarianism of the museums which purchased his artefacts. Considered in its wider nineteenth-century context, this is a representative case study of the early development of archaeology in the wider intellectual, scientific and social context of the era.

Sign in / Sign up

Export Citation Format

Share Document