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Mining high utility mobile sequential patterns in mobile commerce environments
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"... Abstract. Mining user behaviors in mobile environments is an emerging and important topic in data mining fields. Previous researches have combined moving paths and purchase transactions to find mobile sequential patterns. However, these patterns cannot reflect actual profits of items in transaction ..."
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Cited by 5 (1 self)
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Abstract. Mining user behaviors in mobile environments is an emerging and important topic in data mining fields. Previous researches have combined moving paths and purchase transactions to find mobile sequential patterns. However, these patterns cannot reflect actual profits of items in transaction databases. In this work, we explore a new problem of mining high utility mobile sequential patterns by integrating mobile data mining with utility mining. To the best of our knowledge, this is the first work that combines mobility patterns with high utility patterns to find high utility mobile sequential patterns, which are mobile sequential patterns with their utilities. Two tree-based methods are proposed for mining high utility mobile sequential patterns. A series of analyses on the performance of the two algorithms are conducted through experimental evaluations. The results show that the proposed algorithms deliver better performance than the state-of-the-art one under various conditions.
Discovering valuable user behavior patterns in mobile commerce environments
- in PAKDD Workshops
"... 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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Cited by 3 (1 self)
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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. A novel algorithm called UMSPL (high Utility Mobile Sequential Pattern mining by a Level-wised method) is proposed to efficiently find high utility mobile sequential patterns. The experimental results show that the proposed algorithm has excellent performance under various system conditions.
Approaches for Pattern Discovery Using Sequential Data Mining”, Pattern Discovery using Sequential Data Mining
, 2011
"... Sequential pattern mining methods have been found to be applicable in a large number of domains. Sequential data is omnipresent. Sequential pattern mining methods have been used to analyze this data and identify patterns. Such patterns have been used to implement efficient systems that can recommend ..."
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Cited by 2 (0 self)
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Sequential pattern mining methods have been found to be applicable in a large number of domains. Sequential data is omnipresent. Sequential pattern mining methods have been used to analyze this data and identify patterns. Such patterns have been used to implement efficient systems that can recommend based on previously observed patterns, help in making predictions, improve usability of systems, detect events, and in general help in making strategic product decisions. In this chapter, we discuss the applications of sequential data mining in a variety of domains like healthcare, education, Web usage mining, text mining, bioinformatics, telecommunications, intrusion detection, et cetera. We conclude with a summary of the work.
Constraint based Interesting Location and Mobile Web Service Sequence Mining in M
, 2016
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SURVEY ON PERSONAL MOBILE COMMERCE PATTERN MINING AND PREDICTION
"... Abstract-Data Mining refers to extracting or "mining" knowledge from large amounts of data. In this paper we focus on Personal Mobile Commerce Pattern Mining and Prediction. Pattern mining is used to discover patterns to represent the relations among items. Prediction is important in inte ..."
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Abstract-Data Mining refers to extracting or "mining" knowledge from large amounts of data. In this paper we focus on Personal Mobile Commerce Pattern Mining and Prediction. Pattern mining is used to discover patterns to represent the relations among items. Prediction is important in intelligent environment, it captures repetitive patterns or activities and also helps in automating activities. This paper gives a brief introduction to various algorithms and a detailed study has been performed.
An Efficient Framework for Predicting and Recommending M-Commerce Patterns Based on Graph Diffusion Method
, 2013
"... Abstract: Mobile Commerce, also known as M-Commerce or mCommerce, is the ability to conduct commerce using a mobile device. Research is done by Mining and Prediction of Mobile Users' Commerce Behaviors such as their purchase transactions. The problem of PMCP-Mine algorithm has been overcome by ..."
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Abstract: Mobile Commerce, also known as M-Commerce or mCommerce, is the ability to conduct commerce using a mobile device. Research is done by Mining and Prediction of Mobile Users' Commerce Behaviors such as their purchase transactions. The problem of PMCP-Mine algorithm has been overcome by the efficient framework based graph diffusion method. The main objective is to construct the graph based diffusion method. Graph is constructed for the items purchased by the Mobile users and then finding the frequently purchased item. By using ranking method, we are ranking the items based on the transactions. Then, by analyzing the mobile users behavior and recommending the ranked items. This framework produces more efficient and accurate item recommendation than the MCE framework.
A Survey On: Analysis of Pattern Mining and Behavior Prediction in M-Commerce
"... Today is the world of science, mobile technologies, web applications, internet applications, business transactions these technologies can be combined to online business on mobile device which will increase the business performance. This is a real world practical application where a need arises to th ..."
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Today is the world of science, mobile technologies, web applications, internet applications, business transactions these technologies can be combined to online business on mobile device which will increase the business performance. This is a real world practical application where a need arises to think about user using the system application which will run on mobile device. Thinking of user who makes actually transactions with the mobile device is a prime requirement. The main concept is to think about end user who interacts with the system taking into concern of m-commerce and mobile mobility, location area, data mining, behavior of user spending patterns which will have an enormous effect on business industry and to the society also. These methods also increase growth in various kinds of apps in mobile devices from low version phones to smart phones. So a need generates to use these in our transactions to locate shops, malls which will be situated at some distance far from our place and predict the user behavior in purchasing or making transactions into the shop with help of low version phones to smart phones. Since every user does not have smart phones so a system or an application should be made for the user using simple phones to generate automatic view of nearest shops or malls, with help of cellular phone. This will naturally make the business industry grow and give benefit to users which all counts for an m-commerce business economy industry.
Effective Integration of Mobile Applications in the Context of Education System
"... With the advancement of wireless communication and portable devices, mobile users not only access information ubiquitously, but also use mobile devices for business transactions. Users exchange location, preferences and purchase details for discounts and privileges, in commercial applications. Mobil ..."
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With the advancement of wireless communication and portable devices, mobile users not only access information ubiquitously, but also use mobile devices for business transactions. Users exchange location, preferences and purchase details for discounts and privileges, in commercial applications. Mobile applications for educational purposes have plausible prospects. Applications for an educational gaming purpose, a classroom management control, a reference tool and an online study group can be developed. Massive Open Online Courses (MOOCs) aim to bring education to the entire world online, with access to university level courses, thereby promising a quality educational experience. This paper presents the concept applied in M-Commerce transactions, wherein a real time mobile application for an educational portal is developed, to enable e-learning in a wireless environment. It utilizes an Android platform for learners to access and refer online study materials, test themselves through mobile devices, and view their evaluation results. Consequently, intra and interdepartmental group analyses are generated, for assessment by academic heads. It also provides an opportunity to improve students’ performance, for enlightening themselves intellectually and for sharing the knowledge gained among peers. This proposed model could be implemented as a MOOC across groups of institutions, interactive study forums and networking communities.
Hybrid Recommendation Techniques for Improving Wireless Information Delivery
"... Abstract- Wireless Web is an another important area which is consists of much more complex structures and huge collection of ambiguous data. the wireless portion of the web is also quite different from the traditional web. the information contained in the wireless web is often more concise, more loc ..."
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Abstract- Wireless Web is an another important area which is consists of much more complex structures and huge collection of ambiguous data. the wireless portion of the web is also quite different from the traditional web. the information contained in the wireless web is often more concise, more location-specific, and time-critical. the multiplicity of wireless web application among assortment of recommendation aim and technique involve that to gather more and more global recommendation requirements, it is reasonably significant to implement hybrid recommendations for wireless commerce. in this paper, we proposed hybrid recommendation systems in m-commerce, which could integrate multiple association rules together to improve recommendation performance. Our proposed approach based on both straight and meandering rules are attached into one set of global association rules, which might be used for the recommendation of web pages.
