waashk/dblp huggingface dataset:
a unified approach for schema matching, coreference and canonicalization. the automatic consolidation of database records from many heterogeneous sources into a single repository requires solving several information integration tasks. although tasks such as coreference, schema matching, and canonicalization are closely related, they are most commonly studied in isolation. systems that do tackle multiple integration problems traditionally solve each independently, allowing errors to propagate from one task to another. in this paper, we describe a discriminatively-trained model that reasons about schema matching, coreference, and canonicalization jointly. we evaluate our model on a real-world data set of people and demonstrate that simultaneously solving these tasks reduces errors over a cascaded or isolated approach. our experiments show that a joint model is able to improve substantially over systems that either solve each task in isolation or with the conventional cascade. we demonstrate nearly a 50% error reduction for coreference and a 40% error reduction for schema matching.
privacy-preserving cox regression for survival analysis. privacy-preserving data mining (ppdm) is an emergent research area that addresses the incorporation of privacy preserving concerns to data mining techniques. in this paper we propose a privacy-preserving (pp) cox model for survival analysis, and consider a real clinical setting where the data is horizontally distributed among different institutions. the proposed model is based on linearly projecting the data to a lower dimensional space through an optimal mapping obtained by solving a linear programming problem. our approach differs from the commonly used random projection approach since it instead finds a projection that is optimal at preserving the properties of the data that are important for the specific problem at hand. since our proposed approach produces an sparse mapping, it also generates a pp mapping that not only projects the data to a lower dimensional space but it also depends on a smaller subset of the original features (it provides explicit feature selection). real data from several european healthcare institutions are used to test our model for survival prediction of non-small-cell lung cancer patients. these results are also confirmed using publicly available benchmark datasets. our experimental results show that we are able to achieve a near-optimal performance without directly sharing the data across different data sources. this model makes it possible to conduct large-scale multi-centric survival analysis without violating privacy-preserving requirements.
mining multi-faceted overviews of arbitrary topics in a text collection. a common task in many text mining applications is to generate a multi-faceted overview of a topic in a text collection. such an overview not only directly serves as an informative summary of the topic, but also provides a detailed view of navigation to different facets of the topic. existing work has cast this problem as a categorization problem and requires training examples for each facet. this has three limitations: (1) all facets are predefined, which may not fit the need of a particular user. (2) training examples for each facet are often unavailable. (3) such an approach only works for a predefined type of topics. in this paper, we break these limitations and study a more realistic new setup of the problem, in which we would allow a user to flexibly describe each facet with keywords for an arbitrary topic and attempt to mine a multi-faceted overview in an unsupervised way. we attempt a probabilistic approach to solve this problem. empirical experiments on different genres of text data show that our approach can effectively generate a multi-faceted overview for arbitrary topics; the generated overviews are comparable with those generated by supervised methods with training examples. they are also more informative than unstructured flat summaries. the method is quite general, thus can be applied to multiple text mining tasks in different application domains.
mining adaptively frequent closed unlabeled rooted trees in data streams. closed patterns are powerful representatives of frequent patterns, since they eliminate redundant information. we propose a new approach for mining closed unlabeled rooted trees adaptively from data streams that change over time. our approach is based on an efficient representation of trees and a low complexity notion of relaxed closed trees, and leads to an on-line strategy and an adaptive sliding window technique for dealing with changes over time. more precisely, we first present a general methodology to identify closed patterns in a data stream, using galois lattice theory. using this methodology, we then develop three closed tree mining algorithms: an incremental one inctreenat, a sliding-window based one, wintreenat, and finally one that mines closed trees adaptively from data streams, adatreenat. to the best of our knowledge this is the first work on mining frequent closed trees in streaming data varying with time. we give a first experimental evaluation of the proposed algorithms.
pattern-miner: integrated management and mining over data mining models. this demo presents pattern-miner, an integrated environment for pattern management and mining that deals with the whole lifecycle of patterns, from their generation (using data mining techniques) to their storage and querying, putting also emphasis on the comparison between patterns and meta-mining operations over the extracted patterns. pattern comparison (comparing results of the data mining process) and meta-mining are high level pattern operations that can be applied in a variety of applications, from database change management to image comparison and retrieval.
efficient semi-streaming algorithms for local triangle counting in massive graphs. in this paper we study the problem of local triangle counting in large graphs. namely, given a large graph g = (v;e) we want to estimate as accurately as possible the number of triangles incident to every node υ ∈ v in the graph. the problem of computing the global number of triangles in a graph has been considered before, but to our knowledge this is the first paper that addresses the problem of local triangle counting with a focus on the efficiency issues arising in massive graphs. the distribution of the local number of triangles and the related local clustering coefficient can be used in many interesting applications. for example, we show that the measures we compute can help to detect the presence of spamming activity in large-scale web graphs, as well as to provide useful features to assess content quality in social networks. for computing the local number of triangles we propose two approximation algorithms, which are based on the idea of min-wise independent permutations (broder et al. 1998). our algorithms operate in a semi-streaming fashion, using o(jv j) space in main memory and performing o(log jv j) sequential scans over the edges of the graph. the first algorithm we describe in this paper also uses o(jej) space in external memory during computation, while the second algorithm uses only main memory. we present the theoretical analysis as well as experimental results in massive graphs demonstrating the practical efficiency of our approach.
heterogeneous data fusion for alzheimer's disease study. effective diagnosis of alzheimer's disease (ad) is of primary importance in biomedical research. recent studies have demonstrated that neuroimaging parameters are sensitive and consistent measures of ad. in addition, genetic and demographic information have also been successfully used for detecting the onset and progression of ad. the research so far has mainly focused on studying one type of data source only. it is expected that the integration of heterogeneous data (neuroimages, demographic, and genetic measures) will improve the prediction accuracy and enhance knowledge discovery from the data, such as the detection of biomarkers. in this paper, we propose to integrate heterogeneous data for ad prediction based on a kernel method. we further extend the kernel framework for selecting features (biomarkers) from heterogeneous data sources. the proposed method is applied to a collection of mri data from 59 normal healthy controls and 59 ad patients. the mri data are pre-processed using tensor factorization. in this study, we treat the complementary voxel-based data and region of interest (roi) data from mri as two data sources, and attempt to integrate the complementary information by the proposed method. experimental results show that the integration of multiple data sources leads to a considerable improvement in the prediction accuracy. results also show that the proposed algorithm identifies biomarkers that play more significant roles than others in ad diagnosis.
probabilistic latent semantic visualization: topic model for visualizing documents. we propose a visualization method based on a topic model for discrete data such as documents. unlike conventional visualization methods based on pairwise distances such as multi-dimensional scaling, we consider a mapping from the visualization space into the space of documents as a generative process of documents. in the model, both documents and topics are assumed to have latent coordinates in a two- or three-dimensional euclidean space, or visualization space. the topic proportions of a document are determined by the distances between the document and the topics in the visualization space, and each word is drawn from one of the topics according to its topic proportions. a visualization, i.e. latent coordinates of documents, can be obtained by fitting the model to a given set of documents using the em algorithm, resulting in documents with similar topics being embedded close together. we demonstrate the effectiveness of the proposed model by visualizing document and movie data sets, and quantitatively compare it with conventional visualization methods.
temporal pattern discovery for trends and transient effects: its application to patient records. we introduce a novel pattern discovery methodology for event history data focusing explicitly on the detailed temporal relationship between pairs of events. at the core is a graphical statistical approach to summarising and visualising event history data, which contrasts the observed to the expected incidence of the event of interest before and after an index event. thus, pattern discovery is not restricted to a specific time window of interest, but encompasses extended parts of the underlying event histories. in order to effectively screen large collections of event history data for interesting temporal relationships, we introduce a new measure of temporal association. the proposed measure contrasts the observed-to-expected ratio in a time period of interest to that in a pre-defined control period. an important feature of both the observed-to-expected graph itself and the measure of association, is a statistical shrinkage towards the null hypothesis of no association. this provides protection against spurious associations and is an extension of the statistical shrinkage successfully applied to large-scale screening for associations between events in cross-sectional data, such as large collections of adverse drug reaction reports. we demonstrate the usefulness of the proposed pattern discovery methodology by a set of examples from a collection of over two million patient records in the united kingdom. the identified patterns include temporal relationships between drug prescription and medical events suggestive of persistent or transient risks of adverse events, as well as temporal relationships between prescriptions of different drugs.
fast: a roc-based feature selection metric for small samples and imbalanced data classification problems. the class imbalance problem is encountered in a large number of practical applications of machine learning and data mining, for example, information retrieval and filtering, and the detection of credit card fraud. it has been widely realized that this imbalance raises issues that are either nonexistent or less severe compared to balanced class cases and often results in a classifier's suboptimal performance. this is even more true when the imbalanced data are also high dimensional. in such cases, feature selection methods are critical to achieve optimal performance. in this paper, we propose a new feature selection method, feature assessment by sliding thresholds (fast), which is based on the area under a roc curve generated by moving the decision boundary of a single feature classifier with thresholds placed using an even-bin distribution. fast is compared to two commonly-used feature selection methods, correlation coefficient and relevance in estimating features (relief), for imbalanced data classification. the experimental results obtained on text mining, mass spectrometry, and microarray data sets showed that the proposed method outperformed both relief and correlation methods on skewed data sets and was comparable on balanced data sets; when small number of features is preferred, the classification performance of the proposed method was significantly improved compared to correlation and relief-based methods.
on updates that constrain the features' connections during learning. in many multiclass learning scenarios, the number of classes is relatively large (thousands,...), or the space and time efficiency of the learning system can be crucial. we investigate two online update techniques especially suited to such problems. these updates share a sparsity preservation capacity: they allow for constraining the number of prediction connections that each feature can make. we show that one method, exponential moving average, is solving a "discrete" regression problem for each feature, changing the weights in the direction of minimizing the quadratic loss. we design the other method to improve a hinge loss subject to constraints, for better accuracy. we empirically explore the methods, and compare performance to previous indexing techniques, developed with the same goals, as well as other online algorithms based on prototype learning. we observe that while the classification accuracies are very promising, improving over previous indexing techniques, the scalability benefits are preserved.
generating succinct titles for web urls. how can a search engine automatically provide the best and most appropriate title for a result url (link-title) so that users will be persuaded to click on the url? we consider the problem of automatically generating link-titles for urls and propose a general statistical framework for solving this problem. the framework is based on using information from a diverse collection of sources, each of which can be thought of as contributing one or more candidate link-titles for the url. it can also incorporate the context in which the link-title will be used, along with constraints on its length. our framework is applicable to several scenarios: obtaining succinct titles for displaying quicklinks, obtaining titles for urls that lack a good title, constructing succinct sitemaps, etc. extensive experiments show that our method is very effective, producing results that are at least 20% better than non-trivial baselines.
joint latent topic models for text and citations. in this work, we address the problem of joint modeling of text and citations in the topic modeling framework. we present two different models called the pairwise-link-lda and the link-plsa-lda models. the pairwise-link-lda model combines the ideas of lda [4] and mixed membership block stochastic models [1] and allows modeling arbitrary link structure. however, the model is computationally expensive, since it involves modeling the presence or absence of a citation (link) between every pair of documents. the second model solves this problem by assuming that the link structure is a bipartite graph. as the name indicates, link-plsa-lda model combines the lda and plsa models into a single graphical model. our experiments on a subset of citeseer data show that both these models are able to predict unseen data better than the baseline model of erosheva and lafferty [8], by capturing the notion of topical similarity between the contents of the cited and citing documents. our experiments on two different data sets on the link prediction task show that the link-plsa-lda model performs the best on the citation prediction task, while also remaining highly scalable. in addition, we also present some interesting visualizations generated by each of the models.
constructing comprehensive summaries of large event sequences. event sequences capture system and user activity over time. prior research on sequence mining has mostly focused on discovering local patterns appearing in a sequence. while interesting, these patterns do not give a comprehensive summary of the entire event sequence. moreover, the number of patterns discovered can be large. in this article, we take an alternative approach and build short summaries that describe an entire sequence, and discover local dependencies between event types. we formally define the summarization problem as an optimization problem that balances shortness of the summary with accuracy of the data description. we show that this problem can be solved optimally in polynomial time by using a combination of two dynamic-programming algorithms. we also explore more efficient greedy alternatives and demonstrate that they work well on large datasets. experiments on both synthetic and real datasets illustrate that our algorithms are efficient and produce high-quality results, and reveal interesting local structures in the data.
spectral domain-transfer learning. traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain. in many real world applications, however, we wish to make use of the labeled data from one domain (called in-domain) to classify the unlabeled data in a different domain (out-of-domain). this problem often happens when obtaining labeled data in one domain is difficult while there are plenty of labeled data from a related but different domain. in general, this is a transfer learning problem where we wish to classify the unlabeled data through the labeled data even though these data are not from the same domain. in this paper, we formulate this domain-transfer learning problem under a novel spectral classification framework, where the objective function is introduced to seek consistency between the in-domain supervision and the out-of-domain intrinsic structure. through optimization of the cost function, the label information from the in-domain data is effectively transferred to help classify the unlabeled data from the out-of-domain. we conduct extensive experiments to evaluate our method and show that our algorithm achieves significant improvements on classification performance over many state-of-the-art algorithms.
information extraction from wikipedia: moving down the long tail. not only is wikipedia a comprehensive source of quality information, it has several kinds of internal structure (e.g., relational summaries known as infoboxes), which enable self-supervised information extraction. while previous efforts at extraction from wikipedia achieve high precision and recall on well-populated classes of articles, they fail in a larger number of cases, largely because incomplete articles and infrequent use of infoboxes lead to insufficient training data. this paper presents three novel techniques for increasing recall from wikipedia's long tail of sparse classes: (1) shrinkage over an automatically-learned subsumption taxonomy, (2) a retraining technique for improving the training data, and (3) supplementing results by extracting from the broader web. our experiments compare design variations and show that, used in concert, these techniques increase recall by a factor of 1.76 to 8.71 while maintaining or increasing precision.
direct mining of discriminative and essential frequent patterns via model-based search tree. frequent patterns provide solutions to datasets that do not have well-structured feature vectors. however, frequent pattern mining is non-trivial since the number of unique patterns is exponential but many are non-discriminative and correlated. currently, frequent pattern mining is performed in two sequential steps: enumerating a set of frequent patterns, followed by feature selection. although many methods have been proposed in the past few years on how to perform each separate step efficiently, there is still limited success in eventually finding highly compact and discriminative patterns. the culprit is due to the inherent nature of this widely adopted two-step approach. this paper discusses these problems and proposes a new and different method. it builds a decision tree that partitions the data onto different nodes. then at each node, it directly discovers a discriminative pattern to further divide its examples into purer subsets. since the number of examples towards leaf level is relatively small, the new approach is able to examine patterns with extremely low global support that could not be enumerated on the whole dataset by the two-step method. the discovered feature vectors are more accurate on some of the most difficult graph as well as frequent itemset problems than most recently proposed algorithms but the total size is typically 50% or more smaller. importantly, the minimum support of some discriminative patterns can be extremely low (e.g. 0.03%). in order to enumerate these low support patterns, state-of-the-art frequent pattern algorithm either cannot finish due to huge memory consumption or have to enumerate 101 to 103 times more patterns before they can even be found. software and datasets are available by contacting the author.
feedback effects between similarity and social influence in online communities. a fundamental open question in the analysis of social networks is to understand the interplay between similarity and social ties. people are similar to their neighbors in a social network for two distinct reasons: first, they grow to resemble their current friends due to social influence; and second, they tend to form new links to others who are already like them, a process often termed selection by sociologists. while both factors are present in everyday social processes, they are in tension: social influence can push systems toward uniformity of behavior, while selection can lead to fragmentation. as such, it is important to understand the relative effects of these forces, and this has been a challenge due to the difficulty of isolating and quantifying them in real settings. we develop techniques for identifying and modeling the interactions between social influence and selection, using data from online communities where both social interaction and changes in behavior over time can be measured. we find clear feedback effects between the two factors, with rising similarity between two individuals serving, in aggregate, as an indicator of future interaction -- but with similarity then continuing to increase steadily, although at a slower rate, for long periods after initial interactions. we also consider the relative value of similarity and social influence in modeling future behavior. for instance, to predict the activities that an individual is likely to do next, is it more useful to know the current activities of their friends, or of the people most similar to them?