waashk/dblp huggingface dataset:
a cascaded approach to biomedical named entity recognition using a unified model. we propose a cascaded approach for extracting biomedical named entities from text documents using a unified model. previous works often ignore the high computational cost incurred by a single-phase approach. we alleviate this problem by dividing the named entity extraction task into a segmentation task and a classification task, reducing the computational cost by an order of magnitude. a unified model, which we term "maximum-entropy margin-based" (memb), is used in both tasks. the memb model considers the error between a correct and an incorrect output during training and helps improve the performance of extracting sparse entity types that occur in biomedical literature. we report experimental evaluations on the genia corpus available from the bionlp/nlpba (2004) shared task, which demonstrate the state-of-the-art performance achieved by the proposed approach.
using significant, positively associated and relatively class correlated rules for associative classification of imbalanced datasets. the application of association rule mining to classification has led to a new family of classifiers which are often referred to as "associative classifiers (acs)". an advantage of acs is that they are rule-based and thus lend themselves to an easier interpretation. rule-based classifiers can play a very important role in applications such as medical diagnosis and fraud detection where "imbalanced data sets" are the norm and not the exception. the focus of this paper is to extend and modify acs for classification on imbalanced data sets using only statistical techniques. we combine the use of statistically significant rules with a new measure, the class correlation ratio ( ccr), to build an ac which we call sparccc. experiments show that in terms of classification quality, sparccc performs comparably on balanced datasets and outperforms other ac techniques on imbalanced data sets. it also has a significantly smaller rule base and is much more computationally efficient.
language-independent set expansion of named entities using the web. set expansion refers to expanding a given partial set of objects into a more complete set. a well-known example system that does set expansion using the web is google sets. in this paper, we propose a novel method for expanding sets of named entities. the approach can be applied to semi-structured documents written in any markup language and in any human language. we present experimental results on 36 benchmark sets in three languages, showing that our system is superior to google sets in terms of mean average precision.
on meta-learning rule learning heuristics. the goal of this paper is to investigate to what extent a rule learning heuristic can be learned from experience. to that end, we let a rule learner learn a large number of rules and record their performance on the test set. subsequently, we train regression algorithms on predicting the test set performance of a rule from its training set characteristics. we investigate several variations of this basic scenario, including the question whether it is better to predict the performance of the candidate rule itself or of the resulting final rule. our experiments on a number of independent evaluation sets show that the learned heuristics outperform standard rule learning heuristics. we also analyze their behavior in coverage space.
understanding discrete classifiers with a case study in gene prediction. the requirement that the models resulting from data mining should be understandable is an uncontroversial requirement. in the data mining literature, however, it plays hardly any role, if at all. in practice, though, understandability is often even more important than, e.g., accuracy. understandability does not mean that models should be simple. it means that one should be able to understand the predictions of models. in this paper we introduce tools to understand arbitrary classifiers defined on discrete data. more in particular, we introduce explanations that provide insight at a local level. they explain why a classifier classifies a data point as it does. for global insight, we introduce attribute weights. the higher the weight of an attribute, the more often it is decisive in the classification of a data point. to illustrate our tools, we describe a case study in the prediction of small genes. this is a notoriously hard problem in bioinformatics.
origami: mining representative orthogonal graph patterns. in this paper, we introduce the concept of -orthogonal patterns to mine a representative set of graph patterns. intuitively, two graph patterns are -orthogonal if their similarity is bounded above by . each -orthogonal pattern is also a representative for those patterns that are at least similar to it. given user defined , [0, 1], the goal is to mine an -orthogonal, -representative set that minimizes the set of unrepresented patterns. we present origami, an effective algorithm for mining the set of representative orthogonal patterns. origami first uses a randomized algorithm to randomly traverse the pattern space, seeking previously unexplored regions, to return a set of maximal patterns. origami then extracts an orthogonal, -representative set from the mined maximal patterns. we show the effectiveness of our algorithm on a number of real and synthetic datasets. in particular, we show that our method is able to extract high quality patterns even in cases where existing enumerative graph mining methods fail to do so.
structure-based statistical features and multivariate time series clustering. we propose a new method for clustering multivariate time series. a univariate time series can be represented by a fixed-length vector whose components are statistical features of the time series, capturing the global structure. these descriptive vectors, one for each component of the multivariate time series, are concatenated, before being clustered using a standard fast clustering algorithm such as k-means or hierarchical clustering. such statistical feature extraction also serves as a dimension-reduction procedure for multivariate time series. we demonstrate the effectiveness and simplicity of our proposed method by clustering human motion sequences: dynamic and high-dimensional multivariate time series. the proposed method based on univariate time series structure and statistical metrics provides a novel, yet simple and flexible way to cluster multivariate time series data efficiently with promising accuracy. the success of our method on the case study suggests that clustering may be a valuable addition to the tools available for human motion pattern recognition research.
rule cubes for causal investigations. with the complexity of modern vehicles tremendously increasing, quality engineers play a key role within today's automotive industry. field data analysis supports corrective actions in development, production and after sales support. we decompose the requirements and show that association rules, being a popular approach to generating explanative models, still exhibit shortcomings. recently proposed interactive rule cubes are a promising alternative. we extend this work by introducing a way of intuitively visualizing and meaningfully ranking them. moreover, we present methods to interactively factorize a problem and validate hypotheses by ranking patterns based on expectations, and by browsing a cube-based network of related influences. all this is currently in use as an interactive tool for warranty data analysis in the automotive industry. a real-world case study shows how engineers successfully use it in identifying root causes of quality issues.
non-redundant multi-view clustering via orthogonalization. typical clustering algorithms output a single clustering of the data. however, in real world applications, data can often be interpreted in many different ways; data can have different groupings that are reasonable and interesting from different perspectives. this is especially true for high-dimensional data, where different feature subspaces may reveal different structures of the data. why commit to one clustering solution while all these alternative clustering views might be interesting to the user. in this paper, we propose a new clustering paradigm for explorative data analysis: find all non-redundant clustering views of the data, where data points of one cluster can belong to different clusters in other views. we present a framework to solve this problem and suggest two approaches within this framework: (1) orthogonal clustering, and (2) clustering in orthogonal subspaces. in essence, both approaches find alternative ways to partition the data by projecting it to a space that is orthogonal to our current solution. the first approach seeks orthogonality in the cluster space, while the second approach seeks orthogonality in the feature space. we test our framework on both synthetic and high-dimensional benchmark data sets, and the results show that indeed our approaches were able to discover varied solutions that are interesting and meaningful. keywords: multi-view clustering, non-redundant clustering, orthogonalization
analyzing and detecting review spam. mining of opinions from product reviews, forum posts and blogs is an important research topic with many applications. however, existing research has been focused on extraction, classification and summarization of opinions from these sources. an important issue that has not been studied so far is the opinion spam or the trustworthiness of online opinions. in this paper, we study this issue in the context of product reviews. to our knowledge, there is still no published study on this topic, although web page spam and email spam have been investigated extensively. we will see that review spam is quite different from web page spam and email spam, and thus requires different detection techniques. based on the analysis of 5.8 million reviews and 2.14 million reviewers from amazon.com, we show that review spam is widespread. in this paper, we first present a categorization of spam reviews and then propose several techniques to detect them.
detecting subdimensional motifs: an efficient algorithm for generalized multivariate pattern discovery. discovering recurring patterns in time series data is a fundamental problem for temporal data mining. this paper addresses the problem of locating subdimensional motifs in real-valued, multivariate time series, which requires the simultaneous discovery of sets of recurring patterns along with the corresponding relevant dimensions. while many approaches to motif discovery have been developed, most are restricted to categorical data, univariate time series, or multivariate data in which the temporal patterns span all of the dimensions. in this paper, we present an expected linear-time algorithm that addresses a generalization of multivariate pattern discovery in which each motif may span only a subset of the dimensions. to validate our algorithm, we discuss its theoretical properties and empirically evaluate it using several data sets including synthetic data and motion capture data collected by an on-body inertial sensor.
optimal subsequence bijection. we consider the problem of elastic matching of sequences of real numbers. since both a query and a target sequence may be noisy, i.e., contain some outlier elements, it is desirable to exclude the outlier elements from matching in order to obtain a robust matching performance. moreover, in many applications like shape alignment or stereo correspondence it is also desirable to have a one-to-one and onto correspondence (bijection) between the remaining elements. we propose an algorithm that determines the optimal subsequence bijection (osb) of a query and target sequence. the osb is efficiently computed since we map the problem's solution to a cheapest path in a dag (directed acyclic graph). we obtained excellent results on standard benchmark time series datasets. we compared osb to dynamic time warping (dtw) with and without warping window. we do not claim that osb is always superior to dtw. however, our results demonstrate that skipping outlier elements as done by osb can significantly improve matching results for many real datasets. moreover, osb is particularly suitable for partial matching. we applied it to the object recognition problem when only parts of contours are given. we obtained sequences representing shapes by representing object contours as sequences of curvatures.
a novel criterion for onset detection: differential information redundancy with application to human movement initiation. a new detection criterion based on the change in the marginal information redundancy is presented. by establishing a link with information theory we are able to give an intuitive interpretation of our criterion. the usefulness of the new criterion is demonstrated for a case study of human movement initiation detection from force and torque signals in activity of daily living tasks. using the new criterion, we achieve a performance that is more in agreement with expert decisions compared with traditional thresholding techniques and the advanced wavelet-based detector and energy detectors.
an efficient spectral algorithm for network community discovery and its applications to biological and social networks. automatic discovery of community structures in complex networks is a fundamental task in many disciplines, including social science, engineering, and biology. recently, a quantitative measure called modularity (q) has been proposed to effectively assess the quality of community structures. several community discovery algorithms have since been developed based on the optimization of q. however, this optimization problem is np-hard, and the existing algorithms have a low accuracy or are computationally expensive. in this paper, we present an efficient spectral algorithm for modularity optimization. when tested on a large number of synthetic or real-world networks, and compared to the existing algorithms, our method is efficient and and has a high accuracy. in addition, we have successfully applied our algorithm to detect interesting and meaningful community structures from real-world networks in different domains, including biology, medicine and social science. due to space limitation, results of these applications are presented in a complete version of the paper available on our website (http://cse.wustl.edu/~jruan/).
connections between mining frequent itemsets and learning generative models. frequent itemsets mining is a popular framework for pattern discovery. in this framework, given a database of customer transactions, the task is to unearth all patterns in the form of sets of items appearing in a sizable number of transactions. we present a class of models called itemset generating models (or igms) that can be used to formally connect the process of frequent itemsets discovery with the learning of generative models. igms are specified using simple probability mass functions (over the space of transactions), peaked at specific sets of items and uniform everywhere else. under such a connection, it is possible to rigorously associate higher frequency patterns with generative models that have greater data likelihoods. this enables a generative model-learning interpretation of frequent itemsets mining. more importantly, it facilitates a statistical significance test which prescribes the minimum frequency needed for a pattern to be considered interesting. we illustrate the effectiveness of our analysis through experiments on standard benchmark data sets.
local probabilistic models for link prediction. one of the core tasks in social network analysis is to predict the formation of links (i.e. various types of relationships) over time. previous research has generally represented the social network in the form of a graph and has leveraged topological and semantic measures of similarity between two nodes to evaluate the probability of link formation. here we introduce a novel local probabilistic graphical model method that can scale to large graphs to estimate the joint co-occurrence probability of two nodes. such a probability measure captures information that is not captured by either topological measures or measures of semantic similarity, which are the dominant measures used for link prediction. we demonstrate the effectiveness of the co-occurrence probability feature by using it both in isolation and in combination with other topological and semantic features for predicting co-authorship collaborations on three real datasets.
sampling for sequential pattern mining: from static databases to data streams. sequential pattern mining is an active field in the domain of knowledge discovery. recently, with the constant progress in hardware technologies, real-world databases tend to grow larger and the hypothesis that a database can be loaded into main-memory for sequential pattern mining purpose is no longer valid. furthermore, the new model of data as a continuous and potentially infinite flow, known as data stream model, call for a pre-processing step to ease the mining operations. since the database size is the most influential factor for mining algorithms we examine the use of sampling over static databases to get approximate mining results with an upper bound on the error rate. moreover, we extend these sampling analysis and present an algorithm based on reservoir sampling to cope with sequential pattern mining over data streams. we demonstrate with empirical results that our sampling methods are efficient and that sequence mining remains accurate over static databases and data streams.
gapprox: mining frequent approximate patterns from a massive network. recently, there arise a large number of graphs with massive sizes and complex structures in many new applications, such as biological networks, social networks, and the web, demanding powerful data mining methods. due to inherent noise or data diversity, it is crucial to address the issue of approximation, if one wants to mine patterns that are potentially interesting with tolerable variations. in this paper, we investigate the problem of mining frequent approximate patterns from a massive network and propose a method called gapprox. gapprox not only finds approximate network patterns, which is the key for many knowledge discovery applications on structural data, but also enriches the library of graph mining methodologies by introducing several novel techniques such as: (1) a complete and redundancy-free strategy to explore the new pattern space faced by gapprox; and (2) transform "frequent in an approximate sense" into an anti-monotonic constraint so that it can be pushed deep into the mining process. systematic empirical studies on both real and synthetic data sets show that frequent approximate patterns mined from the worm protein-protein interaction network are biologically interesting and gapprox is both effective and efficient.
a computational approach to style in american poetry. we develop a quantitative method to assess the style of american poems and to visualize a collection of poems in relation to one another. qualitative poetry criticism helped guide our development of metrics that analyze various orthographic, syntactic, and phonemic features. these features are used to discover comprehensive stylistic information from a poem's multi-layered latent structure, and to compute distances between poems in this space. visualizations provide ready access to the analytical components. we demonstrate our method on several collections of poetry, showing that it better delineates poetry style than the traditional word-occurrence features that are used in typical text analysis algorithms. our method has potential applications to academic research of texts, to research of the intuitive personal response to poetry, and to making recommendations to readers based on their favorite poems.
locally constrained support vector clustering. support vector clustering transforms the data into a high dimensional feature space, where a decision function is computed. in the original space, the function outlines the boundaries of higher density regions, naturally splitting the data into individual clusters. the method, however, though theoretically sound, has certain drawbacks which make it not so appealing to the practitioner. namely, it is unstable in the presence of outliers and it is hard to control the number of clusters that it identifies. parametrizing the algorithm incorrectly in noisy settings, can either disguise some objectively present clusters in the data, or can identify a large number of small and nonintuitive clusters. here, we explore the properties of the data in small regions building a mixture of factor analyzers. the obtained information is used to regularize the complexity of the outlined cluster boundaries, by assigning suitable weighting to each example. the approach is demonstrated to be less susceptible to noise and to outline better interpretable clusters than support vector clustering alone.
estmax: tracing maximal frequent itemsets over online data streams. in general, the number of frequent itemsets in a data set is very large. in order to represent them in more compact notation, closed or maximal frequent itemsets (mfis) are used. however, the characteristics of a data stream make such a task be more difficult. for this purpose, this paper proposes a method called estmax that can trace the set of mfis over a data stream. the proposed method maintains the set of frequent itemsets by a prefix tree and extracts all of mfis without any additional superset/subset checking mechanism. upon processing a newly generated transaction, its longest matched frequent itemsets are marked in a prefix tree as candidates for mfis. at the same time, if any subset of these newly marked itemsets has been already marked as a candidate mfi, it is cleared as well. by employing this additional step, it is possible to extract the set of mfis at any moment. the performance of the proposed method is comparatively analyzed by a series of experiments to identify its various characteristics.
finding cohesive clusters for analyzing knowledge communities. documents and authors can be clustered into "knowledge communities" based on the overlap in the papers they cite. we introduce a new clustering algorithm, streemer, which finds cohesive foreground clusters embedded in a diffuse background, and use it to identify knowledge communities as foreground clusters of papers which share common citations. to analyze the evolution of these communities over time, we build predictive models with features based on the citation structure, the vocabulary of the papers, and the affiliations and prestige of the authors. findings include that scientific knowledge communities tend to grow more rapidly if their publications build on diverse information and if they use a narrow vocabulary.
zonal co-location pattern discovery with dynamic parameters. zonal co-location patterns represent subsets of featuretypes that are frequently located in a subset of space (i.e., zone). discovering zonal spatial co-location patterns is an important problem with many applications in areas such as ecology, public health, and homeland defense. however, discovering these patterns with dynamic parameters (i.e., repeated specification of zone and interest measure values according to user preferences) is computationally complex due to the repetitive mining process. also, the set of candidate patterns is exponential in the number of feature types, and spatial datasets are huge. previous studies have focused on discovering global spatial co-location patterns with a fixed interest measure threshold. in this paper, we propose an indexing structure for co-location patterns and propose algorithms (zoloc-miner) to discover zonal colocation patterns efficiently for dynamic parameters. extensive experimental evaluation shows our proposed approaches are scalable, efficient, and outperform na¨ive alternatives.