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Frequent sequence mining methods often make use of constraints to control which subsequences should be mined, e.g., length, gap, span, regular-expression, and hierarchy constraints. We show that many subsequence constraints—including and beyond those considered in the literature—can be unified in a single framework. In more detail, we propose a set of simple and intuitive "pattern expressions"...
On social media platforms, companies, organizations and individuals are using the function of sharing or retweeting information to promote their products, policies, and ideas. While a growing body of research has focused on identifying the promoters from millions of users, the promoters themselves are seeking to know what strategies can improve promotional effectiveness, which is rarely studied in...
The Levy Walk (or Levy flight) is a concept fromBiomathematics to describe the hunting–behaviour of manypredatory species. It is a very efficient way to find prey in avery short time frame. We now want to use this concept ina clustering–context to – if you so will – "hunt" for clusters. We describe how we convert this concept into an efficient wayto find cluster centres by linking the data...
Nonnegative matrix factorization (NMF) has beenwidely applied in many domains. In document analysis, it hasbeen increasingly used in topic modeling applications, where aset of underlying topics are revealed by a low-rank factor matrixfrom NMF. However, it is often the case that the resulting topicsgive only general topic information in the data, which tends notto convey much information. To tackle...
In connected health services automatic discovery of recurring patterns and correlations, or insights, provides many interesting opportunities for the personalization of the services. In this paper the focus is on insight mining for a health coaching service. The basic idea in the proposed method is to generate a large number of insight candidates which have been pre-validated with domain experts and...
Typing is a well known concept to prepare services for data processing for instance by choosing the correct service to a mime type for processing. But a lot more metadata elements, like availability and access conditions, provenance, processing preconditions or integrity parameters, are useful to be known in advance for preprocessing data services. In order to expose such metadata independently from...
Semi-supervised learning is the required paradigm when data are partially labeled. It is more adapted for large domain applications when labels are hardly and costly to obtain. In addition, when data are large, feature selection and instance selection are two important dual operations for removing irrelevant information. To address theses challenges together, we propose a unified framework, called...
In this paper, we aimed to guide about latest development and studies about students' performance analysis and Learning Analytics in Massively Open Online Courses (MOOCs) for researchers related with the topics. For this purpose short review for usage of performance prediction and Learning Analytics in MOOCs is investigated In our study, to help readers get familiar with our topic, firstly literature...
Discrimination discovery from data consists of designing data mining methods for the actual discovery of discriminatory situations and practices hidden in a large amount of historical decision records. Approaches based on classification rule mining consider items at a flat concept level, with no exploitation of background knowledge on the hierarchical and inter-relational structure of domains. On...
Aspect Based Sentiment Analysis (ABSA) provides further insight into the analysis of social media. Understanding user opinion about different aspects of products, services or policies can be used for improving and innovating in an effective way. Thus, it is becoming an increasingly important task in the Natural Language Processing (NLP) realm. The standard pipeline of aspect-based sentiment analysis...
In order to generate effective results, it is essential for a recommender system to model the information about the user interests (user profiles). A profile usually contains preferences that reflect the recommendation technique, so collaborative systems represent a user with the ratings given to items, while content-based approaches assign a score to semantic/text-based features of the evaluated...
The educational evaluation requires of analysis and continue strategies to adapted the current context, so that this research presents the need to define models of educational evaluation with adaptive characteristics to the area of knowledge and the students to predict behaviors of academic performance and support the decision making in the educational context. This need is based on the theoretical...
A tool that automatically identifies subject domains of examination questions is useful in at least three ways: (1) it can help learners hone their ability to perform this subject identification task, which is an important skill in several highstakes examinations, (2) in the context of educational content repositories, it can assist both maintainers in organizing and learners in querying content,...
We propose estimators for popular clustering coefficient measures 1) network average clustering coefficient and 2) global clustering coefficient (aka transitivity). Unlike most of previous studies estimating clustering coefficients, we do not use independent vertex sampling as it is either unavailable or inefficient to implement in most Online Social Networks (OSNs). Instead, we propose estimators...
Modern world is not only about software and technology as the world advances it is becoming more data oriented and mathematical in nature. The current size of information that is brought in and processed is large and complex in size. Data size does not only involve using every single point of data that is reported. This information needs to be sized down and understood according to the application...
There is an increasing need to quickly understand the contents log data. A wide range of patterns can be computed and provide valuable information: for example existence of repeated sequences of events or periodic behaviors. However patternminingtechniquesoftenproducemanypatternsthathave to be examined one by one, which is time consuming for experts. On the other hand, visualization techniques are...
Diffusion LMS is an efficient strategy for solving distributed optimization problems with cooperating agents. Nodes are interested in estimating the same parameter vector and exchange information with their neighbors to improve their local estimates. Successful implementation of such applications relies on a substantial amount of communication resources. In this paper, we introduce diffusion LMS strategies...
The consumption of Linked Data has dramatically increased with the increasing momentum towards semantic web. Linked data is essentially a very simplistic format for representation of knowledge in that all the knowledge is represented as triples which can be linked using one or more components from the triple. To date, most of the efforts has been towards either creating linked data by mining the web...
In this work, we describe the design, development, and deployment of NEREA (Named Entity Recognizer for spEcific Areas), an automatic Named Entity Recognizer and Disambiguation system, developed in collaboration with professional documentalists. The aim of NEREA is to keep accurate and current information about the entities mentioned in a local repository, and then support building appropriate infoboxes,...
In recent decades, experts have presented a range of teaching methods and techniques which should increase the learning effectiveness. Naturally, no learning process is universal and reliable at the same time, and suitable for everyone and in every situation. Concept mapping is a method based on findings in cognitive psychology. We investigated how the learning process and effectiveness can be influenced...
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