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MOBILE SEQUENTIAL PATTERN MINING IN LOCATION- BASED SERVICE ENVIRONMENT
"... The advancement of wireless communication techniques and the popularity of mobile devices such as mobile phones, PDA, and GPS-enabled cellular phones, have contributed to a new business model. In this chapter various ..."
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The advancement of wireless communication techniques and the popularity of mobile devices such as mobile phones, PDA, and GPS-enabled cellular phones, have contributed to a new business model. In this chapter various
A Survey on Mining services for Better Enhancement in Small HandHeld Devices
"... This paper presents a case study on data mining services able to support decision makers in strategic planning for the enhancement of small handheld devices. The application provides e-Knowledge services for the analysis of territorial dynamics by processing and modeling huge amount of data, in orde ..."
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This paper presents a case study on data mining services able to support decision makers in strategic planning for the enhancement of small handheld devices. The application provides e-Knowledge services for the analysis of territorial dynamics by processing and modeling huge amount of data, in order to discover rules and patterns in a distributed and heterogeneous content environment. For the analysis of structured data, the application covers the whole Knowledge Discovery process. The purpose of the paper is to show how to implement existing techniques in a flexible architecture for providing new added value services. Finally in our paper, a case study of different data mining task is thrive under different category like in WWW, Mobile environment, PDA Devices, Web log techniques etc. We also use MIDP (Mobile Information device
A Novel RSRM Algorithm for Mining services for Better Enhancement in Small HandHeld Devices
"... This paper presents a case study on data mining services able to support decision makers in strategic planning for the enhancement of small handheld devices. The application provides e-Knowledge services for the analysis of territorial dynamics by processing and modeling huge amount of data, in orde ..."
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This paper presents a case study on data mining services able to support decision makers in strategic planning for the enhancement of small handheld devices. The application provides e-Knowledge services for the analysis of territorial dynamics by processing and modeling huge amount of data, in order to discover rules and patterns in a distributed and heterogeneous content environment. For the analysis of structured data, the application covers the whole Knowledge Discovery process. The purpose of the paper is to show how to implement existing techniques in a flexible architecture for providing new added value services. Finally in our paper, we proposed a RSRM ( Read Subset Removal Miner) algorithm for mining on mobile devices.
1 A One-Phase Method for Mining High Utility Mobile Sequential Patterns in Mobile Commerce Environments
"... Abstract. Mobile sequential pattern mining is an emerging topic in data mining fields with wide applications, such as planning mobile commerce environments and managing online shopping websites. However, an important factor, i.e., actual utilities (i.e., profit here) of items, is not considered and ..."
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Abstract. Mobile sequential pattern mining is an emerging topic in data mining fields with wide applications, such as planning mobile commerce environments and managing online shopping websites. However, an important factor, i.e., actual utilities (i.e., profit here) of items, is not considered and thus some valuable patterns cannot be found. Therefore, previous researches [8, 9] addressed the problem of mining high utility mobile sequential patterns (abbreviated as UMSP). Nevertheless the tree-based algorithms may not perform efficiently since mobile transaction sequences are often too complex to form compress tree structures for analysis. A novel algorithm, namely UM-Span (high Utility Mobile Sequential Pattern mining), is proposed for efficiently mining UMSPs. UM-Span finds UMSPs by a projected database based framework. It does not need additional database scans to find actual UMSPs, which is the bottleneck of utility mining. Experimental results show that UM-Span outperforms the state-of-the-art UMSP mining algorithms under various conditions.
1 Mining Temporal Mobile Sequential Patterns in Location-Based Service Environments
"... In recent years, a number of studies have been done on Location-Based Service (LBS) due to the wide applications. One important research issue is the tracking and prediction of users ’ mobile behavior. In this paper, we propose a novel data mining algorithm named TMSP-Mine for efficiently discoverin ..."
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In recent years, a number of studies have been done on Location-Based Service (LBS) due to the wide applications. One important research issue is the tracking and prediction of users ’ mobile behavior. In this paper, we propose a novel data mining algorithm named TMSP-Mine for efficiently discovering the Temporal Mobile Sequential Patterns (TMSPs) of users in LBS environments. To our best knowledge, this is the first work on mining the mobile sequential patterns associated with moving paths and time intervals in LBS environments. Furthermore, we propose novel location prediction strategies that utilize the discovered TMSPs to effectively predict the next movement of mobile users. Finally, we conducted a series of experiments to evaluate the performance of the proposed method under different system conditions by varying various parameters.
Behavior Patterns in Mobile Commerce Environments
"... Abstract. Mining user behavior patterns in mobile environments is an emerging topic in data mining fields with wide applications. By integrating moving paths with purchasing transactions, one can find the sequential purchasing patterns with the moving paths, which are called mobile sequential patter ..."
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Abstract. Mining user behavior patterns in mobile environments is an emerging topic in data mining fields with wide applications. By integrating moving paths with purchasing transactions, one can find the sequential purchasing patterns with the moving paths, which are called mobile sequential patterns of the mobile users. Mobile sequential patterns can be applied not only for planning mobile commerce environments but also analyzing and managing online shopping websites. However, unit profits and purchased numbers of the items are not considered in traditional framework of mobile sequential pattern mining. Thus, the patterns with high utility (i.e., profit here) cannot be found. In view of this, we aim at integrating mobile data mining with utility mining for finding high utility mobile sequential patterns in this study. Two types of algorithms, namely level-wise and tree-based methods, are proposed for mining high utility mobile sequential patterns. A series of analyses and comparisons on the performance of the two different types of algorithms are conducted through experimental evaluations. The results show that the proposed algorithms
1 Mining Cluster-based Mobile Sequential Patterns in Location-Based Service Environments
"... In recent years, a number of studies have been done on Location-Based Service (LBS) due to their wide range of potential applications. One important research issue in LBS is the mining and predicting of users ’ mobile behaviors. In this paper, we propose a novel data mining algorithm named Cluster-b ..."
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In recent years, a number of studies have been done on Location-Based Service (LBS) due to their wide range of potential applications. One important research issue in LBS is the mining and predicting of users ’ mobile behaviors. In this paper, we propose a novel data mining algorithm named Cluster-based Mobile Sequential Pattern Mine (CMSP-Mine) for efficiently discovering the Cluster-based Mobile Sequential Patterns (CMSPs) of users in LBS environments. In CMSP-Mine, we first propose a transaction similarity measurement named Location-Based Service Alignment (LBS-Alignment) to evaluate the similarity between two mobile transaction sequences. Then, we propose a transaction clustering algorithm named Cluster-Object based Smart Cluster Affinity Search Technique (CO-Smart-CAST) to form a user cluster model of the mobile transactions based on LBS-Alignment. Furthermore, we proposed the novel prediction strategy that utilizes the discovered CMSPs to precisely predict the next movement of mobile users. To our best knowledge, this is the first work on mining the mobile sequential patterns associated with moving path and user groups in LBS environments. Finally, through a series of experiments, our proposed methods were shown to deliver excellent performance in terms of efficiency, accurate and applicability under various system conditions.
Corresponding Author: S. Jacinth Evangeline 30 Efficiently Mining the Frequent Patterns in Mobile Commerce Environment
"... Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT: Nowadays, a rapid development in the commun ..."
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Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT: Nowadays, a rapid development in the communication technology and increasing the usability of powerful portable devices, mobile users can use their mobile devices to access the information. One of the active areas is the mining and prediction of users ’ mobile commerce behaviors such as their movements and purchase transactions. The important issue is to mine the rare frequent items from database to satisfy the user needs. In this paper, we propose a technique that can efficiently satisfy the user needs. It predicts the frequent item based on the user selection. Systolic tree implementation is used to predict the frequently moved item in the database. The main aim is to recommend the stores and items previously to unknown user. We evaluate our system in real world and deliver good performance in terms efficiency and scalability.
Pattern Mining and Prediction
"... Abstract: In this Paper We Propose an application for the mining and prediction of mobile users movements and purchase transactions together. Here we provide an application for the customers Android phone by which they can view the offers provided by various shops at the mall they are at. The applic ..."
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Abstract: In this Paper We Propose an application for the mining and prediction of mobile users movements and purchase transactions together. Here we provide an application for the customers Android phone by which they can view the offers provided by various shops at the mall they are at. The application provides the user with information about which all shops a particular product is available and which shop provides the best offer. By using this application we can make the Live shopping by the customer without having to go to each shop and enquire. Framework for this application having components for finding the similarity between the Items and Stores,efficient mobile commerce pattern mining and a facility for the behaviour prediction of mobile users. In Addition to that planned to add a credit system for the users as well as the prediction of Items and offers based on their Interests which we can collect at the time of registration for the new customers. As of now the experimental evaluation of the Framework have been done. But in this paper I planned to bring the same for Live shopping based on location based systems.
Licensed Under Creative Commons Attribution CC BY Link Prediction in Temporal Mobile Database
"... Abstract: The rapid development of wireless and web technologies has allowed the mobile users to request various kinds of services by mobile devices at anytime and anywhere. The services which are provided to the wireless mobile devices (such as PDAs, Cellular Phones, and Laptops) from anywhere, at ..."
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Abstract: The rapid development of wireless and web technologies has allowed the mobile users to request various kinds of services by mobile devices at anytime and anywhere. The services which are provided to the wireless mobile devices (such as PDAs, Cellular Phones, and Laptops) from anywhere, at any time using ISAP (Information Service and Application Provider) are enhanced by mining and prediction of mobile user behaviors. Given a snapshot of a mobile database, can we infer which customers are likely to access given services in the near future? We formalize this question as the link prediction problem and develop approaches to link prediction based on measures for analyzing the probability of different service access by each customer. Differentiated mobile behaviors among users and temporal periods are not considered simultaneously in the previous works. User relations and temporal property are used simultaneously in this work. Improving the performance of mobile behavior prediction helps the service provider to improve the quality of service. Here, we propose a novel data mining method, namely sequential mobile access pattern (SMAP-Mine) that can efficiently discover mobile users ’ sequential movement patterns associated with requested services. CTMSP-Mine (Cluster-based Temporal Mobile Sequential Pattern- Mine) algorithm is used to mine CTMSPs. In CTMSP-Mine requires user clusters, which are constructed by Cluster-Object-based Smart Cluster Affinity Search Technique (CO-Smart-CAST) and similarities between users are evaluated by Location-Based Service Alignment (LBS-Alignment) to construct the user groups. The temporal property is used by time segmenting the logs using time intervals. The user cluster information resulting from CO-Smart-CAST and the time segmentation table are provided as input to CTMSP-Mine technique, which creates CTMSPs. The prediction strategy uses the patterns to predict the mobile user behavior in the
