waashk/dblp huggingface dataset:
unsupervised cross-domain learning by interaction information co-clustering. in real-world data mining applications, one often has access to multiple datasets that are relevant to the task at hand. however, learning from such datasets can be difficult as they are often drawn from different domains, i.e., not identically distributed or differ in class or feature sets. in this paper, we consider the problem of learning the class structures %, unique and shared, of related domains in an unsupervised manner. its setting generalizes that of information filtering and novelty detection applications which addresses both known and unknown classes. we propose a co-clustering framework for estimating and adapting the class structures of two related domains, {enabling the analyses of shared and unique classes.} we define an objective function using interaction information to take account of the divergence between the corresponding clusters of respective domains. we present an iterative algorithm which alternates object and feature clustering and converges to a local minimum of the objective function. we present empirical results using text benchmarks, comparing the proposed algorithm and combinations of conventional approaches in problems of partitioning documents and detecting unknown topics.
modeling and predicting the helpfulness of online reviews. online reviews provide a valuable resource for potential customers to make purchase decisions. however, the sheer volume of available reviews as well as the large variations in the review quality present a big impediment to the effective use of the reviews, as the most helpful reviews may be buried in the large amount of low quality reviews. the goal of this paper is to develop models and algorithms for predicting the helpfulness of reviews, which provides the basis for discovering the most helpful reviews for given products. we first show that the helpfulness of a review depends on three important factors: the reviewer’s expertise, the writing style of the review, and the timeliness of the review. based on the analysis of those factors, we present a nonlinear regression model for helpfulness prediction. our empirical study on the imdb movie reviews dataset demonstrates that the proposed approach is highly effective.
a probability model for projective clustering on high dimensional data. clustering high dimensional data is a big challenge in data mining due to the curse of dimensionality. to solve this problem, projective clustering has been defined as an extension of traditional clustering that seeks to find projected clusters in subsets of dimensions of a data space. in this paper, the problem of modeling projected clusters is first discussed, and an extended gaussian model is proposed. second, a general objective criterion used with $k$-means type projective clustering is presented based on the model. finally, the expressions to learn model parameters are derived and then used in a new algorithm named fpc to perform fuzzy clustering on high dimensional data. the experimental results on document clustering show the effectiveness of the proposed clustering model.
filling in the blanks - krimp minimisation for missing data. many data sets are incomplete. for correct analysis of such data, one can either use algorithms that are designed to handle missing data or use imputation. imputation has the benefit that it allows for any type of data analysis. obviously, this can only lead to proper conclusions if the provided data completion is both highly accurate and maintains all statistics of the original data. in this paper, we present three data completion methods that are built on the mdl-based {\sc krimp} algorithm. here, we also follow the mdl principle, i.e. the completed database that can be compressed best, is the best completion because it adheres best to the patterns in the data. by using local patterns, as opposed to a global model, krimp captures the structure of the data in detail. experiments show that both in terms of accuracy and expected differences of any marginal, better data reconstructions are provided than the state of the art, structural em.
supervised inductive learning with lotka-volterra derived models. we present a classification algorithm built on our adaptation of the generalized lotka-volterra model, well-known in mathematical ecology. the training algorithm itself consists only of computing several scalars, per each training vector, using a single global user parameter and then solving a linear system of equations. construction of the system matrix is driven by our model and based on kernel functions. the model allows an interesting point of view of kernels' role in the inductive learning process. we describe the model through axiomatic postulates. finally, we present the results of the preliminary validation experiments.
latent dirichlet allocation and singular value decomposition based multi-document summarization. multi-document summarization deals with computing a summary for a set of related articles such that they give the user a general view about the events. one of the objectives is that the sentences should cover the different events in the documents with the information covered in as few sentences as possible. latent dirichlet allocation can breakdown these documents into different topics or events. however to reduce the common information content the sentences of the summary need to be orthogonal to each other since orthogonal vectors have the lowest possible similarity and correlation between them. singular value decompositions used to get the orthogonal representations of vectors and representing sentences as vectors, we can get the sentences that are orthogonal to each other in the lda mixture model weighted term domain. thus using lda we find the different topics in the documents and using svd we find the sentences that best represent these topics. finally we present the evaluation of the algorithms on the duc2002 corpus multi-document summarization tasks using the rouge evaluator to evaluate the summaries. compared to duc 2002 winners, our algorithms gave significantly better rouge-1 recall measures.
cost-sensitive parsimonious linear regression. we examine linear regression problems where some features may only be observable at a cost (e.g., in medical domains where features may correspond to diagnostic tests that take time and costs money). this can be important in the context of data mining, in order to obtain the best predictions from the data on a limited cost budget. we define a parsimonious linear regression objective criterion that jointly minimizes prediction error and feature cost. we modify least angle regression algorithms commonly used for sparse linear regression to produce the parlir algorithm, whichnot only provides an efficient and parsimonious solution as we demonstrate empirically, but it also provides formal guarantees that we prove theoretically.
organic pie charts. we present a new visualization of the distance and cluster structure of high dimensional data. it is particularly well suited for analysis tasks of users unfamiliar with complex data analysis techniques as it builds on the well known concept of pie charts. the non-linear projection capabilities of emergent self-organizing maps (esom) are used to generate a topology-preserving ordering of the data points on a circle. the distance structure within the high dimensional space is visualized on the circle analogously to the u-matrix method for two-dimensional som. the resulting display resembles pie charts but has an organic structure that naturally emerges from the data. pie segments correspond to groups of similar data points. boundaries between segments represent low density regions with larger distances among neighboring points in the high dimensional space. the representation of distances in the form of a periodic sequence of values makes time series segmentation applicable to automated clustering of the data that is in sync with the visualization. we discuss the usefulness of the method on a variety of data sets to demonstrate the applicability in applications such as document analysis or customer segmentation.
graph olap: towards online analytical processing on graphs. olap (on-line analytical processing) is an important notion in data analysis. recently, more and more graph or networked data sources come into being. there exists a similar need to deploy graph analysis from different perspectives and with multiple granularities. however, traditional olap technology cannot handle such demands because it does not consider the links among individual data tuples. in this paper, we develop a novel graph olap framework, which presents a multi-dimensional and multi-level view over graphs. the contributions of this work are two-fold. first, starting from basic definitions, i.e., what are dimensions and measures in the graph olap scenario, we develop a conceptual framework for data cubes on graphs. we also look into different semantics of olap operations, and classify the framework into two major subcases: informational olap and topological olap. then, with more emphasis on informational olap (topological olap will be covered in a future study due to the lack of space), we show how a graph cube can be materialized by calculating a special kind of measure called aggregated graph and how to implement it efficiently. this includes both full materialization and partial materialization where constraints are enforced to obtain an iceberg cube. we can see that the aggregated graphs, which depend on the graph properties of underlying networks, are much harder to compute than their traditional olap counterparts, due to the increased structural complexity of data. empirical studies show insightful results on real datasets and demonstrate the efficiency of our proposed optimizations.
learning on weighted hypergraphs to integrate protein interactions and gene expressions for cancer outcome prediction. building reliable predictive models from multiple complementary genomic data for cancer study is a crucial step towards successful cancer treatment and a full understanding of the underlying biological principles. to tackle this challenging data integration problem, we propose a hypergraph-based learning algorithm called hypergene to integrate microarray gene expressions and protein-protein interactions for cancer outcome prediction and biomarker identification. hypergene is a robust two-step iterative method that alternatively finds the optimal outcome prediction and the optimal weighting of the marker genes guided by a protein-protein interaction network. under the hypothesis that cancer-related genes tend to interact with each other, the hypergene algorithm uses a protein-protein interaction network as prior knowledge by imposing a consistent weighting of interacting genes. our experimental results on two large-scale breast cancer gene expression datasets show that hypergene utilizing a curated protein-protein interaction network achieves significantly improved cancer outcome prediction. moreover, hypergene can also retrieve many known cancer genes as highly weighted marker genes.
evolutionary clustering by hierarchical dirichlet process with hidden markov state. this paper studies evolutionary clustering, which is a recently hot topic with many important applications, noticeably in social network analysis. in this paper, based on the recent literature on hierarchical dirichlet process (hdp) and hidden markov model (hmm), we have developed a statistical model hdp-htm that combines hdp with a hierarchical transition matrix (htm) based on the proposed infinite hierarchical hidden markov state model (ih$^2$ms) as an effective solution to this problem. the hdp-htm model substantially advances the literature on evolutionary clustering in the sense that not only it performs better than the existing literature, but more importantly it is capable of automatically learning the cluster numbers and structures and at the same time explicitly addresses the correspondence issue during the evolution. extensive evaluations have demonstrated the effectiveness and promise of this solution against the state-of-the-art literature.
a practical approach to classify evolving data streams: training with limited amount of labeled data. recent approaches in classifying evolving data streams are based on supervised learning algorithms, which can be trained with labeled data only. manual labeling of data is both costly and time consuming. therefore, in a real streaming environment, where huge volumes of data appear at a high speed, labeled data may be very scarce. thus, only a limited amount of training data may be available for building the classification models, leading to poorly trained classifiers. we apply a novel technique to overcome this problem by building a classification model from a training set having both unlabeled and a small amount of labeled instances. this model is built as micro-clusters using semisupervised clustering technique and classification is performed with κ-nearest neighbor algorithm. an ensemble of these models is used to classify the unlabeled data. empirical evaluation on both synthetic data and real botnet traffic reveals that our approach, using only a small amount of labeled data for training, outperforms state-of-the-art stream classification algorithms that use twenty times more labeled data than our approach.
discovering flow anomalies: a sweet approach. given a percentage-threshold and readings from a pair of consecutive upstream and downstream sensors, flow anomaly discovery identifies dominant time intervals where the fraction of time instants of significantly mis-matched sensor readings exceed the given percentage-threshold. discovering flow anomalies (fa) is an important problem in environmental flow monitoring networks and early warning detection systems for water quality problems. however, mining fas is computationally expensive because of the large (potentially infinite) number of time instants of measurement and potentially long delays due to stagnant (e.g. lakes) or slow moving (e.g. wetland) water bodies between consecutive sensors. traditional outlier detection methods (e.g. t-test) are suited for detecting transient fas (i.e., time instants of significant mis-matches across consecutive sensors) and cannot detect persistent fas (i.e., long variable time-windows with a high fraction of time instant transient fas) due to a lack of a pre-defined window size. in contrast, we propose a smart window enumeration and evaluation of persistence-thresholds (sweet) method to efficiently explore the search space of all possible window lengths. computation overhead is brought down significantly by restricting the start and end points of a window to coincide with transient fas, using a smart counter and efficient pruning techniques. experimental evaluation using a real dataset shows our proposed approach outperforms naıve alternatives.
border sampling through coupling markov chain monte carlo. recently, progressive border sampling (pbs) was proposed for sample selection in supervised learning by progressively learning an augmented full border from small labeled datasets. however, this quadratic learning algorithm is inapplicable to large datasets. in this paper, we incorporate the pbs to a state of the art technique called coupling markov chain monte carlo (cmcmc) in an attempt to scale the original algorithm up on large labeled datasets. the cmcmc can produce an exact sample while a naive strategy for markov chain monte carlo cannot guarantee the convergence to a stationary distribution. the resulting cmcmc-pbs algorithm is thus proposed for border sampling on large datasets. cmcmc-pbs exhibits several remarkable characteristics: linear time complexity, learner-independence, and a consistent convergence to an optimal sample from the original training sets by learning from their subsamples. our experimental results on the 33 either small or large labeled datasets from the ucikdd repository and a nuclear security application show that our new approach outperforms many previous sampling techniques for sample selection.
a topic modeling approach and its integration into the random walk framework for academic search. in this paper, we propose a unified topic modeling approach and its integration into the random walk framework for academic search. specifically, we present a topic model for simultaneously modeling papers, authors, and publication venues. we combine the proposed topic model into the random walk framework. experimental results show that our proposed approach for academic search significantly outperforms the baseline methods of using bm25 and language model, and those of using the existing topic models (including plsi, lda, and the at model).
dirichlet process based evolutionary clustering. evolutionary clustering has emerged as an important research topic in recent literature of data mining, and solutions to this problem have found a wide spectrum of applications, particularly in social network analysis. in this paper, based on the recent literature on dirichlet processes, we have developed two different and specific models as solutions to this problem: dpchain and hdp-evo. both models substantially advance the literature on evolutionary clustering in the sense that not only they both perform better than the existing literature, but more importantly they are capable of automatically learning the cluster numbers and structures during the evolution. extensive evaluations have demonstrated the effectiveness and promise of these models against the state-of-the-art literature.
time sensitive ranking with application to publication search. link-based ranking has contributed significantly to the success of web search. pagerank and hits are the best known link-based ranking algorithms. these algorithms do not consider an important dimension, the temporal dimension. they favor older pages because these pages have many in-links accumulated over time. bringing new and quality pages to the users is important because most users want the latest information. existing remedies to pagerank are mostly heuristic approaches. this paper investigates the temporal aspect of ranking with application to publication search, and proposes a principled method based on the stationary probability distribution of the markov chain. the proposed techniques are evaluated empirically using a large collection of high energy particle physics publication. the results show that the proposed methods are highly effective.
improving collaborative filtering recommendations using external data. this paper describes an approach for incorporating externally specified aggregate ratings information into certain types of collaborative filtering (cf) methods. for a statistical model-based cf approach, we formally showed that this additional aggregated information provides more accurate recommendations of individual items to individual users. furthermore, theoretical insights gained from the analysis of this model-based method suggested a way to incorporate aggregate information into the heuristic item-based cf method. both the model-based and the heuristic item-based cf methods were empirically tested on several datasets, and the experiments uniformly confirmed that the aggregate rating information indeed improves cf recommendations. these results also show the power of theory by demonstrating how the insights gained from theoretical developments can shed light on proper selection of good heuristic methods. we also showed the way to introduce scalability and parallelization into the estimation procedure and reported the running time for steps of the estimation procedure for large datasets.
generalized framework for syntax-based relation mining. supervised approaches to data mining are particularly appealing as they allow for the extraction of complex relations from data objects. in order to facilitate their application in different areas, ranging from protein to protein interaction in bioinformatics to text mining in computational linguistics research, a modular and general mining framework is needed. the major constraint to the generalization process concerns the feature design for the description of relational data. in this paper, we present a machine learning framework for the automatic mining of relations, where the target objects are structurally organized in a tree. object types are generalized by means of the use of roles, whereas the relation properties are described by means of the underlying tree structure. the latter is encoded in the learning algorithm thanks to kernel methods for structured data, which represent structures in terms of their all possible subparts. this approach can be applied to any kind of data disregarding their very nature. experiments with support vector machines on two text mining datasets for relation extraction, i.e. the propbank and framenet corpora, show both that our approach is general, and that it reaches state-of-the-art accuracy.
iterative set expansion of named entities using the web. set expansion refers to expanding a partial set of "seed" objects into a more complete set. one system that does set expansion is seal (set expander for any language), which expands entities automatically by utilizing resources from the web in a language independent fashion. in a previous study, seal showed good set expansion performance using three seed entities; however, when given a larger set of seeds (e.g., ten), seal's expansion method performs poorly. in this paper, we present iterative seal (iseal), which allows a user to provide many seeds. briefly, iseal makes several calls to seal, each call using a small number of seeds. we also show that iseal can be used in a "bootstrapping" manner, where each call to seal uses a mixture of user-provided and self-generated seeds. we show that the bootstrapping version of iseal obtains better results than seal even when using fewer user-provided seeds. in addition, we compare the performance of various ranking algorithms used in iseal, and show that the choice of ranking method has a small effect on performance when all seeds are user-provided, but a large effect when iseal is bootstrapped. in particular, we show that random walk with restart is nearly as good as bayesian sets with user-provided seeds, and performs best with bootstrapped seeds.
a non-parametric semi-supervised discretization method. semi-supervised classification methods aim to exploit labelled and unlabelled examples to train a predictive model. most of these approaches make assumptions on the distribution of classes. this article first proposes a new semi-supervised discretization method which adopts very low informative prior on data. this method discretizes the numerical domain of a continuous input variable, while keeping the information relative to the prediction of classes. then, an in-depth comparison of this semi-supervised method with the original supervised modl approach is presented. we demonstrate that the semi-supervised approach is asymptotically equivalent to the supervised approach, improved with a post-optimizationof the intervals bounds location.