データマイニングにおけるユーザベリティ向上と応用に関する研究

制度:新 ; 文部省報告番号:甲2574号 ; 学位の種類:博士(工学) ; 授与年月日:2008/3/15 ; 早大学位記番号:新473

Pembuatan Website E-Commerce Penjualan Barang Untuk Perusahaan PT. X Dengan Fitur Data Mining Generalize Sequential Pattern

PT. X is a company that sell cars and car\u27s spare parts by retail that use brick and mortar as base. PT. X want to make e-commerce website with hope that the website will help them expanding their market area for facing the rivalry with similar companies.The website poses online market feature that will help the users make their purchase and tracking their transaction they have done, users are divided into two category users and admin, admin are the PT. X promoting and selling their goods, while the users are the one who purchase through the website. This web is scripted using PHP, Ajax, and JavaScript, and user MySQL for creating the database. The development is done using notepad++ software.The data mining feature is based on generalize sequential pattern algorithm, where this feature is used to analyze the customers purchase behaviors and patterns by finding the connection between sold items based on time. Data is processed using the algorithm resulting frequent item sets that will turn into association rules in form of generalize sequential pattern table

A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems

Domain experts should provide relevant domain knowledge to an Intelligent Tutoring System (ITS) so that it can guide a learner during problemsolving learning activities. However, for many ill-defined domains, the domain knowledge is hard to define explicitly. In previous works, we showed how sequential pattern mining can be used to extract a partial problem space from logged user interactions, and how it can support tutoring services during problem-solving exercises. This article describes an extension of this approach to extract a problem space that is richer and more adapted for supporting tutoring services. We combined sequential pattern mining with (1) dimensional pattern mining (2) time intervals, (3) the automatic clustering of valued actions and (4) closed sequences mining. Some tutoring services have been implemented and an experiment has been conducted in a tutoring system.Comment: Proceedings of the 7th Mexican International Conference on Artificial Intelligence (MICAI 2008), Springer, pp. 765-77

Sequential Pattern Mining with Multidimensional Interval Items

In real sequence pattern mining scenarios, the interval information between two item sets is very important. However, although existing algorithms can effectively mine frequent subsequence sets, the interval information is ignored. This paper aims to mine sequential patterns with multidimensional interval items in sequence databases. In order to address this problem, this paper defines and specifies the interval event problem in the sequential pattern mining task. Then, the interval event items framework is proposed to handle the multidimensional interval event items. Moreover, the MII-Prefixspan algorithm is introduced for the sequential pattern with multidimensional interval event items mining tasks. This algorithm adds the processing of interval event items in the mining process. We can get richer and more in line with actual needs information from mined sequence patterns through these methods. This scheme is applied to the actual website behaviour analysis task to obtain more valuable information for web optimization and provide more valuable sequence pattern information for practical problems. This work also opens a new pathway toward more efficient sequential pattern mining tasks

Bidirectional Growth based Mining and Cyclic Behaviour Analysis of Web Sequential Patterns

Web sequential patterns are important for analyzing and understanding users behaviour to improve the quality of service offered by the World Wide Web. Web Prefetching is one such technique that utilizes prefetching rules derived through Cyclic Model Analysis of the mined Web sequential patterns. The more accurate the prediction and more satisfying the results of prefetching if we use a highly efficient and scalable mining technique such as the Bidirectional Growth based Directed Acyclic Graph. In this paper, we propose a novel algorithm called Bidirectional Growth based mining Cyclic behavior Analysis of web sequential Patterns (BGCAP) that effectively combines these strategies to generate prefetching rules in the form of 2-sequence patterns with Periodicity and threshold of Cyclic Behaviour that can be utilized to effectively prefetch Web pages, thus reducing the users perceived latency. As BGCAP is based on Bidirectional pattern growth, it performs only (log n+1) levels of recursion for mining n Web sequential patterns. Our experimental results show that prefetching rules generated using BGCAP is 5-10 percent faster for different data sizes and 10-15% faster for a fixed data size than TD-Mine. In addition, BGCAP generates about 5-15 percent more prefetching rules than TD-Mine.Comment: 19 page

Curricular Analytics in Higher Education

The dissertation addresses different aspects of student success in higher education. Numerous factors may impact a student\u27s ability to succeed and ultimately graduate, including pre-university preparation, as well as the student support services provided by a university. However, even the best efforts to improve in these areas may fail if other institutional factors overwhelm their ability to facilitate student progress. This dissertation addresses this issue from the perspective of curriculum structure. The structural properties of individual curricula are studied, and the extent to which this structure impacts student progress is explored. The structure of curricula are studied using actual university data and analyzed by applying different data mining techniques, machine learning methods and graph theory. These techniques and methods provide a mathematical tool to quantify the complexity of a curriculum structure. The results presented in this work show that there is an inverse correlation between the complexity of a curriculum and the graduation rate of students attempting that curriculum. To make it more practical, this study was extended further to implement a number of predictive models that give colleges and universities the ability to track the progress of their students in order to improve retention and graduation rates. These models accurately predict the performance of students in subsequent terms and accordingly could be used to provide early intervention alerts. The dissertation addresses another important aspect related to curricula. Specifically, how course enrollment sequences in a curriculum impact student progress. Thus, graduation rates could be improved by directing students to follow better course sequences. The novelty of the models presented in this dissertation is characterized in introducing graduation rate, for the first time in literature, from the perspective of curricular complexity. This provides the faculty and staff the ability to better advise students earlier in their academic careers

An Algorithm for Mining High Utility Sequential Patterns with Time Interval

Mining High Utility Sequential Patterns (HUSP) is an emerging topic in data mining which attracts many researchers. The HUSP mining algorithms can extract sequential patterns having high utility (importance) in a quantitative sequence database. In real world applications, the time intervals between elements are also very important. However, recent HUSP mining algorithms cannot extract sequential patterns with time intervals between elements. Thus, in this paper, we propose an algorithm for mining high utility sequential patterns with the time interval problem. We consider not only sequential patterns' utilities, but also their time intervals. The sequence weight utility value is used to ensure the important downward closure property. Besides that, we use four time constraints for dealing with time interval in the sequence to extract more meaningful patterns. Experimental results show that our proposed method is efficient and effective in mining high utility sequential pattern with time intervals

Mining Temporal Sequential Patterns Based on Multi-granularities

Sequential pattern mining is an important data mining problem that can extract frequent subsequences from sequences. However, the times between successive items in a sequence is typically used as user-specified constraints to pre-process the input data or to prune the pattern search space. In either cases, the times cannot be used to identify item intervals of sequential patterns. In this paper, we introduce a form of multi-granularity sequence patterns, which is a sequential pattern where each transition time is annotated with multi-granularity boundary interval and average time derived from the source data rather than the user-predetermined time interval or only a typical time. Then we present a novel algorithm, MG-PrefixSpan, of multiple granularity sequential patterns based on PrefixSpan[, which discovers all such patterns. Empirical evaluation shows that MG-PrefixSpan scales up linearly as the size of database, and has a good scalability with respect to the length of sequence and the size of transaction

Can we Take Advantage of Time-Interval Pattern Mining to Model Students Activity?

International audienceAnalyzing students' activities in their learning process is an issue that has received significant attention in the educational data mining research field. Many approaches have been proposed, including the popular sequential pattern mining. However, the vast majority of the works do not focus on the time of occurrence of the events within the activities. This paper relies on the hypothesis that we can get a better understanding of students' activities, as well as design more accurate models, if time is considered. With this in mind, we propose to study time-interval patterns. To highlight the benefits of managing time, we analyze the data collected about 113 first-year university students interacting with their LMS. Experiments reveal that frequent time-interval patterns are actually identified, which means that some students' activities are regulated not only by the order of learning resources but also by time. In addition, the experiments emphasize that the sets of intervals highly influence the patterns mined and that the set of intervals that represents the human natural time (minute, hour, day, etc.) seems to be the most appropriate one to represent time gap between resources. Finally, we show that time-interval pattern mining brings additional information compared to sequential pattern mining. Indeed, not only the view of students' possible future activities is less uncertain (in terms of learning resources and their temporal gap) but also, as soon as two students differ in their time-intervals, this di↵erence indicates that their following activities are likely to diverge