waashk/dblp huggingface dataset:
supervised tensor learning. this paper aims to take general tensors as inputs for supervised learning. a supervised tensor learning (stl) framework is established for convex optimization based learning techniques such as support vector machines (svm) and minimax probability machines (mpm). within the stl framework, many conventional learning machines can be generalized to take n^th-order tensors as inputs. we also study the applications of tensors to learning machine design and feature extraction by linear discriminant analysis (lda). our method for tensor based feature extraction is named the tenor rank-one discriminant analysis (tr1da). these generalized algorithms have several advantages: 1) reduce the curse of dimension problem in machine learning and data mining; 2) avoid the failure to converge; and 3) achieve better separation between the different categories of samples. as an example, we generalize mpm to its stl version, which is named the tensor mpm (tmpm). tmpm learns a series of tensor projections iteratively. it is then evaluated against the original mpm. our experiments on a binary classification problem show that tmpm significantly outperforms the original mpm.
applying noise handling techniques to genomic data: a case study. osteogenesis imperfecta (oi) is a genetic collagenousdisease associated with mutations in one or both of thegenes colia1 and colia2. there are at least four knownphenotypes of oi, of which type ii is the severest and oftenlethal. we identified three approaches to noise handling,namely, robust algorithms, filtering, and polishing,and evaluated their effectiveness when applied to the problemof classifying the disease oi based on a data set ofamino acid sequences and associated information of pointmutations of colia1. preliminary results suggest that eachnoise handling mechanism can be useful under different circumstances.filtering is stable across all cases. pruningwith robust c4.5 increased the classification accuracy insome cases, and polishing gave rise to some additional improvementin classifying the lethal oi phenotype.
on the mining of substitution rules for statistically dependent items. in this paper, a new mining capability, called mining ofsubstitution rules, is explored. a substitution refers to thechoice made by a customer to replace the purchase of someitems with that of others. the process of mining substitutionrules can be decomposed into two procedures. the first procedureis to identify concrete itemsets among a large numberof frequent itemsets, where a concrete itemset is a frequentitemset whose items are statistically dependent. thesecond procedure is then on the substitution rule generation.two concrete itemsets x and y form a substitutionrule, denoted by x \triangleright y to mean that x is a substitute for y,if and only if (1) x and y are negatively correlated and (2)the negative association rule x \to \overline y exists. in this paper,we derive theoretical properties for the model of substitutionrule mining. then, in light of these properties, algorithmsrm (standing for substitution rule mining) is designedand implemented to discover the substitution rulesefficiently while attaining good statistical significance. empiricalstudies are performed to evaluate the performance ofalgorithm srm proposed. it is shown that algorithm srmproduces substitution rules of very high quality.
a hybrid data-mining approach in genomics and text structures. we introduce a genetic sequence identifier based on ahierarchical system using fuzzy and classic (crisp) neuralnetworks. the system is based on a set of predictors andon a decision network. the prediction of the structure ofthe genes is addressed using a new method and tools,involving the sequence of distances between bases andneuro-fuzzy predictors. the method and system have beensuccessful in predicting genomic sequences and textstructures.
treefinder: a first step towards xml data mining. in this paper, we consider the problem of searching fre-quenttrees from a collection of tree-structured data model-ingxml data. the treef inder algorithm aims at findingtrees, such that their exact or perturbed copies are frequentin a collection of labelled trees.to cope with complexity issues, treef inder is correctbut not complete: it finds a subset of the actually frequenttrees. the default of completeness is experimentally inves-tigatedon artificial medium size datasets; it is shown thattreefinderreaches completeness or falls short to it for arange of experimental settings.
dryade: a new approach for discovering closed frequent trees in heterogeneous tree databases. in this paper we present a novel algorithm for discovering tree patterns in a tree database. this algorithm uses a relaxed tree inclusion definition, making the problem more complex (checking tree inclusion is np-complete), but allowing to mine highly heterogeneous databases. to obtain good performances, our dryade algorithm discovers only closed frequent tree patterns.
efficient mining of high branching factor attribute trees. in this paper, we present a new tree mining algorithm, dryadeparent, based on the hooking principle first introduced in dryade [9]. in the experiments, we demonstrate that the branching factor and depth of the frequent patterns to find are key factor of complexity for tree mining algorithms. we show that dryadeparent outperforms the current fastest algorithm, cmtreeminer, by orders of magnitude on datasets where the frequent patterns have a high branching factor.
mmac: a new multi-class, multi-label associative classification approach. building fast and accurate classifiers for large-scale databases is an important task in data mining. there is growing evidence that integrating classification and association rule mining together can produce more efficient and accurate classifiers than traditional classification techniques. in this paper, the problem of producing rules with multiple labels is investigated. we propose a new associative classification approach called multi-class, multi-label associative classification (mmac). this paper also presents three measures for evaluating the accuracy of data mining classification approaches to a wide range of traditional and multi-label classification problems. results for 28 different datasets show that the mmac approach is an accurate and effective classification technique, highly competitive and scalable in comparison with other classification approaches.
learning bayesian networks from incomplete data based on emi method. currently, there are few efficient methods in practice forlearning bayesian networks from incomplete data, whichaffects their use in real world data mining applications.this paper presents a general-duty method that estimatesthe (conditional) mutual information directly from incompletedatasets, emi. emi starts by computing the intervalestimates of a joint probability of a variable set, which areobtained from the possible completions of the incompletedataset. and then computes a point estimate via a convexcombination of the extreme points, with weights dependingon the assumed pattern of missing data. finally, based onthese point estimates, emi gets the estimated (conditional)mutual information. this paper also applies emi to the dependencyanalysis based learning algorithm by j. cheng soas to efficiently learn bns with incomplete data. the experimentalresults on asia and alarm networks show that emibased algorithm is much more efficient than two search&scoring based algorithms, sem and em-ea algorithms. interms of accuracy, emi based algorithm is more accuratethan sem algorithm, and comparable with em-ea algorithm.
incremental learning of bayesian networks with hidden variables. in this paper, an incremental method for learning bayesian networks based on evolutionary computing, iema, is put forward. iema introduces the evolutionary algorithm and em algorithm into the process of incremental learning, can not only avoid getting into local maxima, but also incrementally learn bayesian networks with high accuracy in presence of missing values andhidden variables. in addition, we improved the incremental learning process by friedman et al. the experimental results verified the validity of iema. in terms of storage cost, iema is comparable with the incremental learning method of friedman et al, while it is ore accurate.
model stability: a key factor in determining whether an algorithm produces an optimal model from a matching distribution. this paper investigates the factors leading to producingsuboptimal models when training and test class distributions(or misclassification costs) are matched. our resultshows that model stability plays a key role in determiningwhether the algorithm produces an optimal modelfrom a matching distribution (cost). the performance differencebetween a model trained from the matching distribution(cost) and the optimal model generally increases asthe degree of model stability decreases. the practical implicationof our result is that one should only follow theconventional wisdom of using a training class distribution(cost) that matches the test class distribution (cost) to traina classifier if the learning algorithm is known to be stable.
combining multiple weak clusterings. a data set can be clustered in many ways dependingon the clustering algorithm employed, parameter settingsused and other factors. can multiple clusterings becombined so that the final partitioning of data providesbetter clustering? the answer depends on the quality ofclusterings to be combined as well as the properties of thefusion method. first, we introduce a unifiedrepresentation for multiple clusterings and formulate thecorresponding categorical clustering problem. as aresult, we show that the consensus function is related tothe classical intra-class variance criterion using thegeneralized mutual information definition. second, weshow the efficacy of combining partitions generated byweak clustering algorithms that use data projections andrandom data splits. a simple explanatory model is offeredfor the behavior of combinations of such weak clusteringcomponents. we analyze the combination accuracy as afunction of parameters controlling the power andresolution of component partitions as well as the learningdynamics vs. the number of clusterings involved. finally,some empirical studies compare the effectiveness ofseveral consensus functions.
analysis of consensus partition in cluster ensemble. in combination of multiple partitions, one is usually interested in deriving a consensus solution with a quality better than that of given partitions. several recent studies have empirically demonstrated improved accuracy of clustering ensembles on a number of artificial and real-world data sets. unlike certain multiple supervised classifier systems, convergence properties of unsupervised clustering ensembles remain unknown for conventional combination schemes. in this paper we present formal arguments on the effectiveness of cluster ensemble from two perspectives. the first is based on a stochastic partition generation model related to re-labeling and consensus function with plurality voting. the second is to study the property of the "mean" partition of an ensemble with respect to a metric on the space of all possible partitions. in both the cases, the consensus solution can be shown to converge to a true underlying clustering solution as the number of partitions in the ensemble increases. this paper provides a rigorous justification for the use of cluster ensemble.
anomaly intrusion detection using multi-objective genetic fuzzy system and agent-based evolutionary computation framework. in this paper, we present a multi-objective genetic fuzzy system for anomaly intrusion detection. the proposed system extracts accurate and interpretable fuzzy rule-based knowledge from network data using an agent-based evolutionary computation framework. the experimental results on kdd-cup99 intrusion detection benchmark data demonstrate that our system can achieve high detection rate for intrusion attacks and low false positive rate for normal network traffic.
enhancing techniques for efficient topic hierarchy integration. in this paper, we study the problem of integrating documentsfrom different sources into a comprehensive topic hierarchy.our objective is to develop efficient techniques thatimprove the accuracy of traditional categorization methodsby incorporating categorization information providedby data sources into categorization process. notice thatin the world-wide web, categorization information is oftenavailable from information sources. we present severalenhancing techniques that use categorization informationto enhance traditional methods such as naive bayes andsupport vector machines. experiment on collections fromopenfind and yam, and google and yahoo!, well-knownpopular web sites in taiwan and usa, respectively, showsthat our techniques significantly improve the classificationaccuracy from, for example, 55% to 66% for naive bayes,and from 57% to 67% for svm for the data set collectedfrom yam and openfind.
mining associated implication networks: computational intermarket analysis. current attempts to analyze international financialmarkets include the use of financial technical analysis anddata mining techniques. in this paper, we propose a newapproach that incorporates implication networks andassociation rules to form an associated network structure.the proposed approach explicitly addresses the issue oflocal vs. global influences between financial markets.
mining frequent closed itemsets with the frequent pattern list. the mining of the complete set of frequent itemsets willlead to a huge number of itemsets. fortunately, thisproblem can be reduced to the mining of frequent closeditemsets (fcis), which results in a much smaller number ofitemsets. the approaches to mining frequent closeditemsets can be categorized into two groups: those withcandidate generation and those without. in this paper, wepropose an approach to mining frequent closed itemsetswithout candidate generation: with a data structure calledthe frequent pattern list (fpl). we designed thealgorithm fplc -mining to mine the frequent closeditemsets (fcis). experimental result shows that our methodis faster than the previously existing ones.
visualization of rule's similarity using multidimensional scaling. one of the most important problems with rule inductionmethods is that it is very difficult for domain experts to checkmillions of rules generated from large datasets. the discoveryfrom these rules requires deep interpretation from domainknowledge. although several solutions have been proposedin the studies on data mining and knowledge discovery,these studies are not focused on similarities betweenrules obtained. when one rule r1 has reasonable featuresand the other rule r2 with high similarity to r1 includes unexpectedfactors, the relations between these rules will becomea trigger to the discovery of knowledge. in this paper,we propose a visualization approach to show the similarrelations between rules based on multidimensional scaling,which assign a two-dimensional cartesian coordinateto each data point from the information about similiariesbetween this data and others data. we evaluated this methodon two medical data sets, whose experimental results showthat knowledge useful for domain experts could be found.
pattern discovery based on rule induction and taxonomy generation. one of the most important problems with rule inductionmethods is that they cannot extract rules, which plausiblyrepresent experts' decision processes. in this paper,the characteristics of experts' rules are closely examinedand a new approach to extract plausible rules is introduced,which consists of the following three procedures. first, thecharacterization of decision attributes (given classes) is extractedfrom databases and the concept hierarchy for givenclasses is calculated. second, based on the hierarchy, rulesfor each hierarchical level are induced from data. then, foreach given class, rules for all the hierarchical levels are integratedinto one rule.
a greedy algorithm for selecting models in ensembles. we are interested in ensembles of models built over k data sets. common approaches are either to combine models by vote averaging, or to build a meta-model on the outputs of the local models. in this paper, we consider the model assignment approach, in which a meta-model selects one of the local statistical models for scoring. we introduce an algorithm called greedy data labeling (gdl) that improves the initial data partition by reallocating some data, so that when each model is built on its local data subset, the resulting hierarchical system has minimal error. we present evidence that model assignment may in certain situations be more natural than traditional ensemble learning, and if enhanced by gdl, it often outperforms traditional ensembles.
tsp: mining top-k closed sequential patterns. sequential pattern mining has been studied extensivelyin data mining community.most previous studies requirethe specification of a minimum support threshold to performthe mining.however, it is difficult for users to providean appropriate threshold in practice.to overcomethis difficulty, we propose an alternative task: mining top-kfrequent closed sequential patterns of length no less thanmin_l, where k is the desired number of closed sequentialpatterns to be mined, and min_l is the minimum length ofeach pattern.we mine closed patterns since they are compactrepresentations of frequent patterns.we developed an efficient algorithm, called tsp, whichmakes use of the length constraint and the properties of top-kclosed sequential patterns to perform dynamic support-raisingand projected database-pruning.our extensive performancestudy shows that tsp outperforms the closed sequentialpattern mining algorithm even when the latter isrunning with the best tuned minimum support threshold.
privacy-preserving outlier detection. outlier detection can lead to the discovery of truly unexpected knowledge in many areas such as electronic commerce, credit card fraud and especially national security. we look at the problem of finding outliers in large distributed databases where privacy/security concerns restrict the sharing of data. both homogeneous and heterogeneous distribution of data is considered. we propose techniques to detect outliers in such scenarios while giving formal guarantees on the amount of information disclosed.
computing frequent graph patterns from semistructured data. whereas data mining in structured data focuses on frequentdata values, in semi-structured and graph data theemphasis is on frequent labels and common topologies.here, the structure of the data is just as important as its content.we study the problem of discovering typical patterns ofgraph data. the discovered patterns can be useful for manyapplications, including: compact representation of sourceinformation and a road-map for browsing and querying informationsources. difficulties arise in the discovery taskfrom the complexity of some of the required sub-tasks, suchas sub-graph isomorphism. this paper proposes a new algorithmfor mining graph data, based on a novel definitionof support. empirical evidence shows practical, as well astheoretical, advantages of our approach.
estimating the number of segments in time series data using permutation tests. segmentation is a popular technique for discoveringstructure in time series data. we address the largely openproblem of estimating the number of segments that can bereliably discovered. we introduce a novel method for theproblem, called pete. pete is based on permutation testing.the problem is an instance of model (dimension) selection.the proposed method analyzes the possible overfitof a model to the available data rather than uses a termfor penalizing model complexity. in this respect the approachis more similar to cross-validation than regulariza-tionbased techniques (e.g., aic, bic, mdl, mml). further,the method produces a p value for each increase in thenumber of segments. this gives the user an overview of thestatistical significance of the segmentations. we evaluatethe performance of the proposed method using both syntheticand real time series data. the experiments show thatpermutation testing gives realistic results about the numberof reliably identifiable segments and that it compares favorablywith the monte carlo cross-validation (mccv) andcommonly used bic criteria.
active sampling for feature selection. in knowledge discovery applications, where new featuresare to be added, an acquisition policy can help select thefeatures to be acquired based on their relevance and thecost of extraction. this can be posed as a feature selectionproblem where the feature values are not known in advance.we propose a technique to actively sample the featurevalues with the ultimate goal of choosing between alternativecandidate features with minimum sampling cost.our heuristic algorithm is based on extracting candidatefeatures in a region of the instance space where the featurevalue is likely to alter our knowledge the most. an experimentalevaluation on a standard database shows that it ispossible outperform a random subsampling policy in termsof the accuracy in feature selection.
mining web data to create online navigation recommendations. a system to provide online navigation recommendation for web visitors is introduced. we call visitor the anonymous user, i.e., when only data about her/his browsing behavior (web logs) are available. we first apply clustering techniques over a large sample of web data. next, from thesignificant patterns that are discovered, a set of rules about how to use them is created. finally, comparing the current web visitor session with the patterns, online navigation recommendations are proposed using the mentioned rules. the system was tested using data from a real web site, showing its effectiveness.
combining the web content and usage mining to understand the visitor behavior in a web site. a web site is a semi structured collection of differentkinds of data, whose motivation is show relevant informationto visitor and by this way capture her/his attention.understand the specifics preferences that define the visitorbehavior in a web site, is a complex task. an approximationis suppose that it depend the content, navigationsequence and time spent in each page visited. these variablescan be extracted from the web log files and the website itself, using web usage and content mining respectively.combining the describe variables, a similarity measureamong visitor sessions is introduced and used in a clusteringalgorithm, which identifies groups of similar sessions,allowing the analysis of visitors behavior.in order to prove the methodology's effectiveness, it wasapplied in a certain web site, showing the benefits of thedescribed approach.
web cartography for online state promotion: an algorithm for clustering web resources. this paper presents an approach of web cartography to be used in the context of online site promotion.the overall objective is to provide users with handy maps offering information about candidate sites for the creation of hyperlinks that enable a large flow of targeted visitors.two main types of data must be considered; texts and hyperlinks.we propose to exploit the latter to construct a relevant corpus on which semantic as well as graph analysis can be applied.the stress is put on theclustering of web resources based on the link network,which makes it possible to highlight groups of strongly connected sites which are of the utmost interest for our application.to tackle the site graph partitioning problem, we turn to a promising iterative approach initially developedin the context of computer-aided design.it uses spectral decomposition of the laplacian matrix to embed theconsidered graph in a geometric space where efficientmethods can be applied.an algorithm that was adaptedfrom an existing one implements the method.experimentswere conductedon a real application case concerning the promotion of a site dealing with cognac.we present the obtained map as well as leads to exploit it.