waashk/dblp huggingface dataset:
aggregation-based feature invention and relational concept classes. model induction from relational data requires aggregation of the values of attributes of related entities. this paper makes three contributions to the study of relational learning. (1) it presents a hierarchy of relational concepts of increasing complexity, using relational schema characteristics such as cardinality, and derives classes of aggregation operators that are needed to learn these concepts. (2) expanding one level of the hierarchy, it introduces new aggregation operators that model the distributions of the values to be aggregated and (for classification problems) the differences in these distributions by class. (3) it demonstrates empirically on a noisy business domain that more-complex aggregation methods can increase generalization performance. constructing features using target-dependent aggregations can transform relational prediction tasks so that well-understood feature-vector-based modeling algorithms can be applied successfully.
data-driven validation, completion and construction of event relationship networks. event management is a focal point in building and maintaining high quality information infrastructures. we have witnessed the shift of the paradigm of event management in practice from root cause analysis (rca) to action-oriented analysis (aoa). ibm has developed a pioneer event management methodology (emd) based on the aoa paradigm and applied it to more than two hundred production sites with success. foreseeably, more and more event management professionals will apply aoa in different incarnations in building proactive management facilities. by that, building correct and effective event relationship networks (erns) becomes the dominating activity in aoa service design process. currently, the quality of erns and the cost of building them largely depend on the knowledge of domain experts. we believe that we can utilize historical event logs in shortening the erns design process and perfecting the quality of erns. in this paper, we describe in detail how to apply this data-driven approach in ern validation, completion and construction.
new unsupervised clustering algorithm for large datasets. a fast and accurate unsupervised clustering algorithm has been developed for clustering very large datasets. though designed for very large volumes of geospatial data, the algorithm is general enough to be used in a wide variety of domain applications. the number of computations the algorithm requires is ~ o(n), and thus faster than hierarchical algorithms. unlike the popular k-means heuristic, this algorithm does not require a series of iterations to converge to a solution. in addition, this method does not depend on initialization of a given number of cluster representatives, and so is insensitive to initial conditions. being unsupervised, the algorithm can also "rank" each cluster based on density. the method relies on weighting a dataset to grid points on a mesh, and using a small number of rule-based agents to find the high density clusters. this method effectively reduces large datasets to the size of the grid, which is usually many orders of magnitude smaller. numerical experiments are shown that demonstrate the advantages of this algorithm over other techniques.
mining rare and frequent events in multi-camera surveillance video using self-organizing maps. this paper describes a method for unsupervised classification of events in multi-camera indoors surveillance video. this research is a part of the multiple sensor indoor surveillance (msis) project which uses 32 axis-2100 webcams that observe an office environment. the research was inspired by the following practical problem: how automatically classify and visualize a 24 hour long video captured by 32 cameras? raw data are sequences of jpeg images captured by webcams at the rate 2-6 hz. the following features are extracted from the image data: foreground pixels' spatial distribution and color histogram. the data are integrated by event by averaging motion and color features and creating a "summary" frame which accumulates all foreground pixels of frames of the event into one image. the self-organizing map (som) approach is applied to event data for clustering and visualization. one-level and two-level som clustering are used. a tool for browsing results allows exploring units of the som maps at different levels of hierarchy, clusters of units and distances between units in 3d space. a special technique has been developed to visualize rare events. the results are presented and discussed.
improving discriminative sequential learning with rare--but--important associations. discriminative sequential learning models like conditional random fields (crfs) have achieved significant success in several areas such as natural language processing or information extraction. their key advantage is the ability to capture various non--independent and overlapping features of inputs. however, several unexpected pitfalls have a negative influence on the model's performance; these mainly come from an imbalance among classes/labels, irregular phenomena, and potential ambiguity in the training data. this paper presents a data--driven approach that can deal with such hard--to--predict data instances by discovering and emphasizing rare--but--important associations of statistics hidden in the training data. mined associations are then incorporated into these models to deal with difficult examples. experimental results of english phrase chunking and named entity recognition using crfs show a significant improvement in accuracy. in addition to the technical perspective, our approach also highlights a potential connection between association mining and statistical learning by offering an alternative strategy to enhance learning performance with interesting and useful patterns discovered from large dataset.
b-em: a classifier incorporating bootstrap with em approach for data mining. this paper investigates the problem of augmenting labeled data with unlabeled data to improve classification accuracy. this is significant for many applications such as image classification where obtaining classification labels is expensive, while large unlabeled examples are easily available. we investigate an expectation maximization (em) algorithm for learning from labeled and unlabeled data. the reason why unlabeled data boosts learning accuracy is because it provides the information about the joint probability distribution. a theoretical argument shows that the more unlabeled examples are combined in learning, the more accurate the result. we then introduce b-em algorithm, based on the combination of em with bootstrap method, to exploit the large unlabeled data while avoiding prohibitive i/o cost. experimental results over both synthetic and real data sets that the proposed approach has a satisfactory performance.
making every bit count: fast nonlinear axis scaling. existing axis scaling and dimensionality methods focus on preserving structure, usually determined via the euclidean distance. in other words, they inherently assume that the euclidean distance is already correct. we instead propose a novel nonlinear approach driven by an information-theoretic viewpoint, which we show is also strongly linked to intrinsic dimensionality, or degrees of freedom; and uniformity. nonlinear transformations based on common probability distributions, combined with information-driven selection, simultaneously reduce the number of dimensions required and increase the value of those we retain. experiments on real data confirm that this approach reveals correlations, finds novel attributes, and scales well.
is there a grand challenge or x-prize for data mining? this panel will discuss possible exciting and motivating grand challenge problems for data mining, focusing on bioinformatics, multimedia mining, link mining, text mining, and web mining.
capturing best practice for microarray gene expression data analysis. analyzing gene expression data from microarray devices has many important application in medicine and biology, but presents significant challenges to data mining. microarray data typically has many attributes (genes) and few examples (samples), making the process of correctly analyzing such data difficult to formulate and prone to common mistakes. for this reason it is unusually important to capture and record good practices for this form of data mining. this paper presents a process for analyzing microarray data, including pre-processing, gene selection, randomization testing, classification and clustering; this process is captured with "clementine application templates". the paper describes the process in detail and includes three case studies, showing how the process is applied to 2-class classification, multi-class classification and clustering analyses for publicly available microarray datasets.
bayesian analysis of massive datasets via particle filters. markov chain monte carlo (mcmc) techniques revolutionized statistical practice in the 1990s by providing an essential toolkit for making the rigor and flexibility of bayesian analysis computationally practical. at the same time the increasing prevalence of massive datasets and the expansion of the field of data mining has created the need to produce statistically sound methods that scale to these large problems. except for the most trivial examples, current mcmc methods require a complete scan of the dataset for each iteration eliminating their candidacy as feasible data mining techniques.in this article we present a method for making bayesian analysis of massive datasets computationally feasible. the algorithm simulates from a posterior distribution that conditions on a smaller, more manageable portion of the dataset. the remainder of the dataset may be incorporated by reweighting the initial draws using importance sampling. computation of the importance weights requires a single scan of the remaining observations. while importance sampling increases efficiency in data access, it comes at the expense of estimation efficiency. a simple modification, based on the "rejuvenation" step used in particle filters for dynamic systems models, sidesteps the loss of efficiency with only a slight increase in the number of data accesses.to show proof-of-concept, we demonstrate the method on a mixture of transition models that has been used to model web traffic and robotics. for this example we show that estimation efficiency is not affected while offering a 95% reduction in data accesses.
incorporating prior knowledge with weighted margin support vector machines. like many purely data-driven machine learning methods, support vector machine (svm) classifiers are learned exclusively from the evidence presented in the training dataset; thus a larger training dataset is required for better performance. in some applications, there might be human knowledge available that, in principle, could compensate for the lack of data. in this paper, we propose a simple generalization of svm: weighted margin svm (wmsvms) that permits the incorporation of prior knowledge. we show that sequential minimal optimization can be used in training wmsvm. we discuss the issues of incorporating prior knowledge using this rather general formulation. the experimental results show that the proposed methods of incorporating prior knowledge is effective.
learning sparse metrics via linear programming. calculation of object similarity, for example through a distance function, is a common part of data mining and machine learning algorithms. this calculation is crucial for efficiency since distances are usually evaluated a large number of times, the classical example being query-by-example (find objects that are similar to a given query object). moreover, the performance of these algorithms depends critically on choosing a good distance function. however, it is often the case that (1) the correct distance is unknown or chosen by hand, and (2) its calculation is computationally expensive (e.g., such as for large dimensional objects). in this paper, we propose a method for constructing relative-distance preserving low-dimensional mapping (sparse mappings). this method allows learning unknown distance functions (or approximating known functions) with the additional property of reducing distance computation time. we present an algorithm that given examples of proximity comparisons among triples of objects (object i is more like object j than object k), learns a distance function, in as few dimensions as possible, that preserves these distance relationships. the formulation is based on solving a linear programming optimization problem that finds an optimal mapping for the given dataset and distance relationships. unlike other popular embedding algorithms, this method can easily generalize to new points, does not have local minima, and explicitly models computational efficiency by finding a mapping that is sparse, i.e. one that depends on a small subset of features or dimensions. experimental evaluation shows that the proposed formulation compares favorably with a state-of-the art method in several publicly available datasets.
robust boosting and its relation to bagging. several authors have suggested viewing boosting as a gradient descent search for a good fit in function space. at each iteration observations are re-weighted using the gradient of the underlying loss function. we present an approach of weight decay for observation weights which is equivalent to "robustifying" the underlying loss function. at the extreme end of decay this approach converges to bagging, which can be viewed as boosting with a linear underlying loss function. we illustrate the practical usefulness of weight decay for improving prediction performance and present an equivalence between one form of weight decay and "huberizing" --- a statistical method for making loss functions more robust.
estimating the size of the telephone universe: a bayesian mark-recapture approach. mark-recapture models have for many years been used to estimate the unknown sizes of animal and bird populations. in this article we adapt a finite mixture mark-recapture model in order to estimate the number of active telephone lines in the usa. the idea is to use the calling patterns of lines that are observed on the long distance network to estimate the number of lines that do not appear on the network. we present a bayesian approach and use markov chain monte carlo methods to obtain inference from the posterior distributions of the model parameters. at the state level, our results are in fairly good agreement with recent published reports on line counts. for lines that are easily classified as business or residence, the estimates have low variance. when the classification is unknown, the variability increases considerably. results are insensitive to changes in the prior distributions. we discuss the significant computational and data mining challenges caused by the scale of the data, approximately 350 million call-detail records per day observed over a number of weeks.
cluster-based concept invention for statistical relational learning. we use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning. entities derived from clusters increase the expressivity of feature spaces by creating new first-class concepts which contribute to the creation of new features. for example, in citeseer, papers can be clustered based on words or citations giving "topics", and authors can be clustered based on documents they co-author giving "communities". such cluster-derived concepts become part of more complex feature expressions. out of the large number of generated features, those which improve predictive accuracy are kept in the model, as decided by statistical feature selection criteria. we present results demonstrating improved accuracy on two tasks, venue prediction and link prediction, using citeseer data.
evaluation of prediction models for marketing campaigns. we consider prediction-model evaluation in the context of marketing-campaign planning. in order to evaluate and compare models with specific campaign objectives in mind, we need to concentrate our attention on the appropriate evaluation-criteria. these should portray the model's ability to score accurately and to identify the relevant target population. in this paper we discuss some applicable model-evaluation and selection criteria, their relevance for campaign planning, their robustness under changing population distributions, and their employment when constructing confidence intervals. we illustrate our results with a case study based on our experience from several projects.
short term performance forecasting in enterprise systems. we use data mining and machine learning techniques to predict upcoming periods of high utilization or poor performance in enterprise systems. the abundant data available and complexity of these systems defies human characterization or static models and makes the task suitable for data mining techniques. we formulate the problem as one of classification: given current and past information about the system's behavior, can we forecast whether the system will meet its performance targets over the next hour? using real data gathered from several enterprise systems in hewlett-packard, we compare several approaches ranging from time series to bayesian networks. besides establishing the predictive power of these approaches our study analyzes three dimensions that are important for their application as a stand alone tool. first, it quantifies the gain in accuracy of multivariate prediction methods over simple statistical univariate methods. second, it quantifies the variations in accuracy when using different classes of system and workload features. third, it establishes that models induced using combined data from various systems generalize well and are applicable to new systems, enabling accurate predictions on systems with insufficient historical data. together this analysis offers a promising outlook on the development of tools to automate assignment of resources to stabilize performance, (e.g., adding servers to a cluster) and allow opportunistic job scheduling (e.g., backups or virus scans).
customer lifetime value modeling and its use for customer retention planning. we present and discuss the important business problem of estimating the effect of retention efforts on the lifetime value of a customer in the telecommunications industry. we discuss the components of this problem, in particular customer value and length of service (or tenure) modeling, and present a novel segment-based approach, motivated by the segment-level view marketing analysts usually employ. we then describe how we build on this approach to estimate the effects of retention on lifetime value. our solution has been successfully implemented in amdocs' business insight (bi) platform, and we illustrate its usefulness in real-world scenarios.
visualizing concept drift. we describe a visualization technique that uses brushed, parallel histograms to aid in understanding concept drift in multidimensional problem spaces. this technique illustrates the relationship between changes in distributions of multiple antecedent feature values and the outcome distribution. we can also observe effects on the relative utilization of predictive rules. our parallel histogram technique solves the over-plotting difficulty of parallel coordinate graphs and the difficulty of comparing distributions of brushed and original data. we demonstrate our technique's usefulness in understanding concept drifts in power demand and stock investment returns.
identifying early buyers from purchase data. market research has shown that consumers exhibit a variety of different purchasing behaviors; specifically, some tend to purchase products earlier than other consumers. identifying such early buyers can help personalize marketing strategies, potentially improving their effectiveness. in this paper, we present a non-parametric approach to the problem of identifying early buyers from purchase data. our formulation takes as inputs the detailed purchase information of each consumer, with which we construct a weighted directed graph whose nodes correspond to consumers and whose edges correspond to purchases consumers have in common; the edge weights indicate how frequently consumers purchase products earlier than other consumers.identifying early buyers corresponds to the problem of finding a subset of nodes in the graph with maximum difference between the weights of the outgoing and incoming edges. this problem is a variation of the maximum cut problem in a directed graph. we provide an approximation algorithm based on semidefinite programming (sdp) relaxations pioneered by goemans and williamson, and analyze its performance. we apply the algorithm to real purchase data from amazon.com, providing new insights into consumer behaviors.
critical event prediction for proactive management in large-scale computer clusters. as the complexity of distributed computing systems increases, systems management tasks require significantly higher levels of automation; examples include diagnosis and prediction based on real-time streams of computer events, setting alarms, and performing continuous monitoring. the core of autonomic computing, a recently proposed initiative towards next-generation it-systems capable of 'self-healing', is the ability to analyze data in real-time and to predict potential problems. the goal is to avoid catastrophic failures through prompt execution of remedial actions.this paper describes an attempt to build a proactive prediction and control system for large clusters. we collected event logs containing various system reliability, availability and serviceability (ras) events, and system activity reports (sars) from a 350-node cluster system for a period of one year. the 'raw' system health measurements contain a great deal of redundant event data, which is either repetitive in nature or misaligned with respect to time. we applied a filtering technique and modeled the data into a set of primary and derived variables. these variables used probabilistic networks for establishing event correlations through prediction algorithms. we also evaluated the role of time-series methods, rule-based classification algorithms and bayesian network models in event prediction.based on historical data, our results suggest that it is feasible to predict system performance parameters (sars) with a high degree of accuracy using time-series models. rule-based classification techniques can be used to extract machine-event signatures to predict critical events with up to 70% accuracy.
query chains: learning to rank from implicit feedback. this paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. we observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. to validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments. we also implemented a real-world search engine to test our approach, using a modified ranking svm to learn an improved ranking function from preference data. our results demonstrate significant improvements in the ranking given by the search engine. the learned rankings outperform both a static ranking function, as well as one trained without considering query chains.
incentive networks. we propose a notion of incentive networks, modeling online settings in which multiple participants in a network help each other find information. within this general setting, we study query incentive networks, a natural abstraction of question-answering systems with rewards for finding answers. we analyze strategic behavior in such networks and under a simple model of networks, show that the nash equilibrium for participants' strategies exhibits an unexpected threshold phenomenon.