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Networks naturally capture a host of real-world interactions, from social interactions and email communication to brain activity. However, graphs are not always directly observed, especially in scientific domains, such as neuroscience, where monitored brain activity is often captured as time series. How can we efficiently infer networks from time series data (e.g., model the functional organization...
This article is a comprehensive literature review of student-facing learning analytics reporting systems that track learning analytics data and report it directly to students. This literature review builds on four previously conducted literature reviews in similar domains. Out of the 945 articles retrieved from databases and journals, 93 articles were included in the analysis. Articles were coded...
With the goal of helping software engineering researchers understand how to improve their papers, Mary Shaw presented "Writing Good Software Engineering Research Papers" in 2003. Shaw analyzed the abstracts of the papers submitted to the 2002 International Conference of Software Engineering (ICSE) to determine trends in research question type, contribution type, and validation approach....
Doing data science - extracting insight by analyzing data - is not easy. Data science is used to answer interesting questions that typically involve multiple diverse data sources, many different types of analysis, and often, large and messy data volumes. To answer one of these questions, several types of expertise may be needed to understand the context and domain being served, to import and transform...
Many books and papers describe how to do data science. While those texts are useful, it can also be important to reflect on anti-patterns; i.e. common classes of errors seen when large communities of researchers and commercial software engineers use, and misuse data mining tools. This technical briefing will present those errors and show how to avoid them.
For the shortcoming of the traditional focused crawler, this paper proposed an improved focused crawl method which based on syntactic dependency analysis. This method generates a words collection of the text through TF-IDF algorithm and generates a phrases collection through syntactic dependency analysis firstly. Then evaluate the collection of words and phrases to select set of keywords of the text...
Graphs are widely used to represent many differentkinds of real world data such as social networks, protein-proteininteractions, and road networks. In many cases, each node in agraph is associated with a set of its attributes and it is criticalto not only consider the link structure of a graph but also usethe attribute information to achieve more meaningful results invarious graph mining tasks. Most...
Based on the concept of isomorphism of relations, a relation is turned into a simplicial complex, which is a combinatorial representation of a polyhedron. So frequent itemsets mining is transform turned into geometric traversal problem. By leveraging on geometric structure of simplicial complex, a very fast algorithm for traversal is found; it is based on a geometric concept, called sub-cone construction...
In recent years the social network analysis has been increased, in this paper we focus mining the small network formed by social gathering or business meeting. The main objective is to discover the existence of correlation and influence among the group of people by analysing their social affinity and emotions, currently we are interested only in facial emotions. Whereas the approach for emotion detection...
Evolving graphs arise in problems where interrelations between data change over time. We present a breadth first search (BFS) algorithm for evolving graphs that computes the most direct influences between nodes at two different times. Using simple examples, we show that naive unfoldings of adjacency matrices miscount the number of temporal paths. By mapping an evolving graph to an adjacency matrix...
In this study we analyzed the curricula of 65 university students to investigate the impact of activities progression on student performances. Clustering curricula based on activity order and type we discovered a significant incidence on performance, validating the predictive power of curricula. Nevertheless, we discovered that the characterization of clusters is mainly due to non mandatory activities,...
Advancements in social media technology have resulted in the booming of massive public data. The availability of these huge data sets offers numerous research opportunities for deriving meaningful cause-effect relationships for many applications. One important application domain is the cause of side effects of drugs. In this paper, we applied supervised learning to extract useful cause-and-effect...
In this paper we present the case study on application of data mining techniques like clustering, regression and scoring with heuristic measures for the largest social event prognosis based on spatiotemporal data. The aim of this analysis is to propose the methodology that bases on archival records and is capable to predict the place where the largest social event will take place and who will be a...
In this paper, we proposed a novel method (CompUXLSA) to predict user experience from reviews sentences using Latent Semantic Analysis (LSA). Human uses words to represent or express thoughts. The “word of mouth” could influence others especially through web and social media, which are the common communication tools today. We believe that reviews can be categorized according to user experiences since...
The rising accessibility and popularity of gambling products has increased interest in the effects of gambling. Nonetheless, research of gambling measures is scarce. This paper presents the application of data mining techniques, on 46,514 gambling sessions, to distinguish types of gambling and identify potential instances of problem gambling in EGMs. Gambling sessions included measures of gambling...
Learning to classify new (target) data in a different domain is always an interesting and challenging task in data mining. The classifier could suffer the dataset bias when predicting the new categories from target domain. Many adaptation methods have been proposed to adjust this bias but are limited to using data either from similar categories or requiring a large number of labeled examples from...
In recent years, the introduction of data analytics to large amounts of healthcare data collected on daily basis opened numerous new opportunities and challenges in the field of medical informatics. By definition, healthcare informatics refers to the process of leveraging information technologies to improve the quality of healthcare. Many researchers are focusing on basic and translational research...
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Process mining provides new ways to utilize the abundance of data in enterprises. Suddenly many organizations realize thatsurvival is not possible without exploiting available data intelligently. A new profession is emerging: the data scientist. Justlike computer science emerged as a new discipline from mathematics when computers became abundantly available, we nowsee the birth of data science as...
In this digital era most of the information is made available in digital form. For many years, people have held the hypothesis that using phrases for a representation of document and topic should perform better than terms. In this paper we are examine and investigate this fact with considering several state of art datamining methods that gives satisfactory results to improve the effectiveness of the...
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