Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

/pdf/educational-data-mining-a-review-of-the-state-of-the-art-51aglgywl8.pdf

Educational Data Mining: A Review of the State of the Art

https://bura.brunel.ac.uk/bitstream/2438/13152/4/Fulltext.pdf

Critical analysis of Big Data challenges and analytical methods

Time-series clustering - A decade review

第12次 아시아ㆍ太平洋 矯政局長會議 參加記

One of the main unresolved problems that arise during the data mining process is treating data that contains temporal information. In this case, a complete understanding of the entire phenomenon requires that the data should be viewed as a sequence of events. Temporal sequences appear in a vast range of domains, from engineering, to medicine and finance, and the ability to model and extract information from them is crucial for the advance of the information society. This paper provides a survey on the most significant techniques developed in the past ten years to deal with temporal sequences.

Temporal Data Mining: an overview

The problem of sequential pattern mining is one of the several that has deserved particular attention on the general area of data mining. Despite the important developments in the last years, the best algorithm in the area (Prefix-Span) does not deal with gap constraints and consequently doesn't allow for the introduction of background knowledge into the process. In this paper we present the generalization of the PrefixSpan algorithm to deal with gap constraints, using a new method to generate projected databases. Studies on performance and scalability were conducted in synthetic and real-life datasets, and the respective results are presented.

/pdf/generalization-of-pattern-growth-methods-for-sequential-1bsahe8fnq.pdf

Generalization of pattern-growth methods for sequential pattern mining with gap constraints

A structured view on pattern mining-based biclustering

Increased application of structured pattern mining requires a perfect understanding of the problem and a clear identification of the advantages and disadvantages of existing algorithms. Among those algorithms, pattern-growth methods have been shown to have the best performance when applied to sequential pattern mining. However, their advantages over apriori-based methods are not well explained and understood. Detailed analysis of the performance and memory requirements for these algorithms shows that counting the support for each potential pattern is the most computationally demanding step. Additionally, the analysis makes clear that the main advantage of patterngrowth over apriori-based methods resides on the restriction of the search space that is obtained from the creation of projected databases. In this paper, we present this analysis and describe how apriori-based algorithms can achieve the efficiency of pattern-growth methods.

/pdf/sequential-pattern-mining-algorithms-trade-offs-between-3pkijtg7vr.pdf

Sequential Pattern Mining Algorithms: Trade-offs between Speed and Memory

One of the unresolved problems faced in the construction of intelligent tutoring systems is the acquisition of background knowledge, either for the specification of the teaching strategy, or for the construction of the student model, identifying the deviations of students’ behavior. In this paper, we argue that the use of sequential pattern mining and constraint relaxations can be used to automatically acquire that knowledge. We show that the methodology of constrained pattern mining used can solve this problem in a way that is difficult to achieve with other approaches.

/pdf/acquiring-background-knowledge-for-intelligent-tutoring-54ce28l8iw.pdf

Cláudia Antunes

Papers

Temporal Data Mining: an overview

Generalization of pattern-growth methods for sequential pattern mining with gap constraints

A structured view on pattern mining-based biclustering

Sequential Pattern Mining Algorithms: Trade-offs between Speed and Memory

Acquiring Background Knowledge for Intelligent Tutoring Systems.