Visual mining in music collections with Emergent SOM

Different methods of organizing large collections of music with databionic mining techniques are described. The Emergent Self-Organizing Map is used to cluster and visualize similar artists and songs. The first method is the MusicMiner system that utilizes semantic descriptions learned from low level audio features for each song. The second method uses tags that have been assigned to music artists by the users of the social music platform Last.fm. For both methods we demonstrate the visualization capabilities of the U-Map. An intuitive browsing of large music collections is offered based on the paradigm of topographic maps. The semantic concepts behind the features enhance the interpretability of the maps

Anticipating annotations and emerging trends in biomedical literature

The BioJournalMonitor is a decision support system for the analysis of trends and topics in the biomedical literature. Its main goal is to identify potential diagnostic and therapeu-tic biomarkers for specific diseases. Several data sources are continuously integrated to provide the user with up-to-date information on current research in this field. State-of-the-art text mining technologies are deployed to provide added value on top of the original content, including named en-tity detection, relation extraction, classification, clustering, ranking, summarization, and visualization. We present two novel technologies that are related to the analysis of tem-poral dynamics of text archives and associated ontologies. Currently, the MeSH ontology is used to annotate the sci-entific articles entering the PubMed database with medical terms. Both the maintenance of the ontology as well as the annotation of new articles is performed largely manually. We describe how probabilistic topic models can be used to anno-tate recent articles with the most likely MeSH terms. This provides our users with a competitive advantage because, when searching for MeSH terms, articles are found long be-fore they are manually annotated. We further present a study on how to predict the inclusion of new terms in the MeSH ontology. The results suggest that early prediction of emerging trends is possible. The trend ranking functions are deployed in our system to enable interactive searches for the hottest new trends relating to a disease

Grand Challenges in global eye health: a global prioritisation process using Delphi method

Background We undertook a Grand Challenges in Global Eye Health prioritisation exercise to identify the key issues that must be addressed to improve eye health in the context of an ageing population, to eliminate persistent inequities in health-care access, and to mitigate widespread resource limitations. Methods Drawing on methods used in previous Grand Challenges studies, we used a multi-step recruitment strategy to assemble a diverse panel of individuals from a range of disciplines relevant to global eye health from all regions globally to participate in a three-round, online, Delphi-like, prioritisation process to nominate and rank challenges in global eye health. Through this process, we developed both global and regional priority lists. Findings Between Sept 1 and Dec 12, 2019, 470 individuals complete round 1 of the process, of whom 336 completed all three rounds (round 2 between Feb 26 and March 18, 2020, and round 3 between April 2 and April 25, 2020) 156 (46%) of 336 were women, 180 (54%) were men. The proportion of participants who worked in each region ranged from 104 (31%) in sub-Saharan Africa to 21 (6%) in central Europe, eastern Europe, and in central Asia. Of 85 unique challenges identified after round 1, 16 challenges were prioritised at the global level; six focused on detection and treatment of conditions (cataract, refractive error, glaucoma, diabetic retinopathy, services for children and screening for early detection), two focused on addressing shortages in human resource capacity, five on other health service and policy factors (including strengthening policies, integration, health information systems, and budget allocation), and three on improving access to care and promoting equity. Interpretation This list of Grand Challenges serves as a starting point for immediate action by funders to guide investment in research and innovation in eye health. It challenges researchers, clinicians, and policy makers to build collaborations to address specific challenge

Time series feature extraction for data mining using DWT and DFT

A new method of dimensionality reduction for time series data mining is proposed. Each time series is compressed with wavelet or Fourier decomposition. Instead of using only the first coefficients, a new method of choosing the best coefficients for a set of time series is presented. A criterion function is evaluated using all values of a coefficient position to determine a good set of coefficients. The optimal criterion function with respect to energy preservation is given. For many real life data sets much more energy can be preserved, which is advantageous for data mining tasks. All time series to be mined, or at least a representative subset, need to be available a priori

Time series knowledge mining

An important goal of knowledge discovery is the search for patterns in data that can help explain the underlying process that generated the data. The patterns are required to be new, useful, and understandable to humans. In this work we present a new method for the understandable description of local temporal relationships in multivariate data, called Time Series Knowledge Mining (TSKM). We define the Time Series Knowledge Representation (TSKR) as a new language for expressing temporal knowledge. The patterns have a hierarchical structure, each level corresponds to a single temporal concept. On the lowest level, intervals are used to represent duration. Overlapping parts of intervals represent coincidence on the next level. Several such blocks of intervals are connected with a partial order relation on the highest level. Each pattern element consists of a semiotic triple to connect syntactic and semantic information with pragmatics. The patterns are very compact, but offer details for each element on demand. In comparison with related approaches, the TSKR is shown to have advantages in robustness, expressivity, and comprehensibility. Efficient algorithms for the discovery of the patterns are proposed. The search for coincidence as well as partial order can be formulated as variants of the well known frequent itemset problem. One of the best known algorithms for this problem is therefore adapted for our purposes. Human interaction is used during the mining to analyze and validate partial results as early as possible and guide further processing steps. The efficacy of the methods is demonstrated using several data sets. In an application to sports medicine the results were recognized as valid and useful by an expert of the field

Mining hierarchical temporal patterns in multivariate time series

Abstract. The Unification-based Temporal Grammar is a temporal extension of static unification-based grammars. It defines a hierarchical temporal rule language to express complex patterns present in multivariate time series. The Temporal Data Mining Method is the accompanying framework to discover temporal knowledge based on this rule language. A semiotic hierarchy of temporal patterns, which are not a priori given, is build in a bottom up manner from static logical descriptions of multivariate time instants. We demonstrate the methods using music data, extracting typical parts of songs.

Efficient mining of understandable patterns from multivariate interval time series

Abstract. We present a new method for the understandable description of local temporal relationships in multivariate data, called Time Series Knowledge Mining (TSKM). We define the Time Series Knowledge Representation (TSKR) as a new language for expressing temporal knowledge in time interval data. The patterns have a hierarchical structure, with levels corresponding to the temporal concepts duration, coincidence, and partial order. The patterns are very compact, but offer details for each element on demand. In comparison with related approaches, the TSKR is shown to have advantages in robustness, expressivity, and comprehensibility. The search for coincidence and partial order in interval data can be formulated as instances of the well known frequent itemset problem. Efficient algorithms for the discovery of the patterns are adapted accordingly. A novel form of search space pruning effectively reduces the size of the mining result to ease interpretation and speed up the algorithms. Human interaction is used during the mining to analyze and validate partial results as early as possible and guide further processing steps. The efficacy of the methods is demonstrated using two real life data sets. In an application to sports medicine the results were recognized as valid and useful by an expert of the field. Keywords: knowledge discovery, time series, interval patterns, Allen’s relations