waashk/dblp huggingface dataset:
a comparative study of rnn for outlier detection in data mining. we have proposed replicator neural networks (rnns)for outlier detection [8]. here we compare rnn for outlierdetection with three other methods using both publiclyavailable statistical datasets (generally small) and datamining datasets (generally much larger and generally realdata). the smaller datasets provide insights into the relativestrengths and weaknesses of rnns. the larger datasetsparticularly test scalability and practicality of application.
center-based indexing for nearest neighbors search. the paper addresses the problem of indexing data forthe k nearest neighbors (k-nn) search. it presents a tree-basedtop-down indexing method that uses an iterative k-meansalgorithm for tree node splitting and combines threedifferent search pruning criteria from bst, ght and gnatinto one. the experiments show that the presented indexingtree accelerates the k-nn searching up to several thousandstimes in case of large data sets.
association rule mining in peer-to-peer systems. we extend the problem of association rule mining -a key data mining problem - to systems in which thedatabase is partitioned among a very large number ofcomputers that are dispersed over a wide area. such computing systems include grid computing platforms, federated database systems, and peer-to-peer computing environments. the scale of these systems poses several difficulties, such as the impracticality of global communications and global synchronization, dynamic topology changes ofthe network, on-the-fly data updates, the need to share resources with other applications, and the frequent failureand recovery of resources.we present an algorithm by which every node in thesystem can reach the exact solution, as if it were giventhe combined database. the algorithm is entirely asynchronous, imposes very little communication overhead,transparently tolerates network topology changes andnode failures, and quickly adjusts to changes in the dataas they occur. simulation of up to 10,000 nodes show thatthe algorithm is local: all rules, except for those whoseconfidence is about equal to the confidence threshold, arediscovered using information gathered from a very smallvicinity, whose size is independent of the size of the system.
mpis: maximal-profit item selection with cross-selling considerations. in the literature of data mining, many different algorithmsfor association rule mining have been proposed. however,there is relatively little study on how association rules can aidin more specific targets. in this paper, one of the applicationsfor association rules - maximal-profit item selection with cross-selling effect (mpis) problem - is investigated. the problemis about selecting a subset of items which can give the maximalprofit with the consideration of cross-selling. we provethat a simple version of this problem is np-hard. we proposea new approach to the problem with the consideration of theloss rule - a kind of association rule to model the cross-sellingeffect. we show that the problem can be transformed to aquadratic programming problem. in case quadratic programmingis not applicable, we also propose a heuristic approach.experiments are conducted to show that both of the proposedmethods are highly effective and efficient.
adapting information extraction knowledge for unseen web sites. we propose a wrapper adaptation framework which aimsat adapting a learned wrapper to an unseen web site. it significantlyreduces human effort in constructing wrappers.our framework makes use of extraction rules previously discoveredfrom a particular site to seek potential training ex-amplecandidates for an unseen site. rule generalizationand text categorization are employed for finding suitable examplecandidates. another feature of our approach is thatit makes use of the previously discovered lexicon to classifygood training examples automatically for the new site. weconducted extensive experiments to evaluate the quality ofthe extraction performance and the adaptability of our approach.
a probabilistic approach for adapting information extraction wrappers and discovering new attributes. we develop a probabilistic framework for adapting information extraction wrappers with new attribute discovery. wrapper adaptation aims at automatically adapting a previously learned wrapper from the source web site to a new unseen site for information extraction. one unique characteristic of our framework is that it can discover new or previously unseen attributes as well as headers from the new site. it is based on a generative model for the generation of text fragments related to attribute items and formatting data in a web page. to solve the wrapper adaptation problem, we consider two kinds of information from the source web site. the first kind of information is the extraction knowledge contained in the previously learned wrapper from the source web site. the second kind of information is the previously extracted or collected items. we employ a bayesian learning approach to automatically select a set of training examples for adapting a wrapper for the new unseen site. to solve the new attribute discovery problem, we develop a model which analyzes the surrounding text fragments of the attributes in the new unseen site. a bayesian learning method is developed to discover the new attributes and their headers. em technique is employed in both bayesian learning models. we conducted extensive experiments from a number of real-world web sites to demonstrate the effectiveness of our framework.
hot item mining and summarization from multiple auction web sites. online auction web sites are fast changing, highly dynamic, and complex as they involve tremendous sellers and potential buyers, as well as a huge amount of items listed for bidding. we develop a two-phase framework which aims at mining and summarizing hot items from multiple auctionweb sites to assist decision making. the objective of the first phase is to automatically extract the product features and product feature values of the items from the descriptions provided by the sellers. we design a hmm-based learning method to train an extended hmm model which can adapt to the unseen web page from which the information is extracted. the goal of the second phase is to discover and summarize the hot items based on the extracted information. we formulate the hot item mining task as a semi-supervised learning problem and employ the graph mincuts algorithm to accomplish this task. the summary of the hot items is then generated by considering the frequency and the position of the product features being mentioned in the descriptions. we have conducted extensive experiments from several real-world auction web sites to demonstrate the effectiveness of our framework.
a hybrid approach to discover bayesian networks from databases using evolutionary programming. this paper describes a novel data mining approach thatemploys evolutionary programming to discover knowledgerepresented in bayesian networks. there are two differentapproaches to the network learning problem. the first oneuses dependency analysis, while the second one searchesgood network structures according to a metric. unfortu-nately,both approaches have their own drawbacks. thus,we propose a novel hybrid algorithm of the two approaches,which consists of two phases, namely, the conditional inde-pendence(ci) test and the search phases. a new opera-toris introduced to further enhance the search efficiency.we conduct a number of experiments and compare the hy-bridalgorithm with our previous algorithm, mdlep [18],which uses ep for network learning. the empirical resultsillustrate that the new approach has better performance.we apply the approach to a data sets of direct marketingand compare the performance of the evolved bayesian net-worksobtained by the new algorithm with the models gen-eratedby other methods. in the comparison, the inducedbayesian networks produced by the new algorithm outper-formthe other models.
merging interface schemas on the deep web via clustering aggregation. we consider the problem of integrating a large number of interface schemas over the deep web, the scale of the problem and the diversity of the sources present serious challenges to the conventional manual or rule-based approaches to schema integration. to address these challenges, we propose a novel formulation of schema integration as an optimization problem, with the objective of maximally satisfying the constraints given by individual schemas. since the optimization problem can be shown to be np-complete, we develop a novel approximation algorithm lmax, which builds the unified schema via recursive applications of clustering aggregation. we further extend lmax to handle the irregularities frequently occurring among the interface schemas. extensive evaluation on real-world data sets shows the effectiveness of our approach.
using category-based adherence to cluster market-basket data. in this paper, we devise an efficient algorithm for clusteringmarket-basket data. different from those of the traditionaldata, the features of market-basket data are knownto be of high dimensionality, sparsity, and with massive out-liers.without explicitly considering the presence of the tax-onomy,most prior efforts on clustering market-basket datacan be viewed as dealing with items in the leaf level of thetaxonomy tree. clustering transactions across different levelsof the taxonomy is of great importance for marketingstrategies as well as for the result representation of the clusteringtechniques for market-basket data. in view of thefeatures of market-basket data, we devise in this paper anovel measurement, called the category-based adherence,and utilize this measurement to perform the clustering. thedistance of an item to a given cluster is defined as the numberof links between this item and its nearest large node inthe taxonomy tree where a large node is an item (i.e., leaf)or a category (i.e., internal) node whose occurrence countexceeds a given threshold. the category-based adherenceof a transaction to a cluster is then defined as the averagedistance of the items in this transaction to that cluster.with this category-based adherence measurement, wedevelop an efficient clustering algorithm, called algorithmcba (standing for category-based adherence), for market-basketdata with the objective to minimize the category-basedadherence. a validation model based on informationgain (ig) is also devised to assess the quality of clusteringfor market-basket data. as validated by both real and syntheticdatasets, it is shown by our experimental results, withthe taxonomy information, algorithm cba devised in thispaper significantly outperforms the prior works in both theexecution efficiency and the clustering quality for market-basketdata.
mining strong affinity association patterns in data sets with skewed support distribution. existing association-rule mining algorithms often relyon the support-based pruning strategy to prune its combinatorialsearch space. this strategy is not quite effectivefor data sets with skewed support distributions because theytend to generate many spurious patterns involving itemsfrom different support levels or miss potentially interestinglow-support patterns. to overcome these problems, we proposethe concept of hyperclique pattern, which uses an objectivemeasure called h-confidence to identify strong affinitypatterns. we also introduce the novel concept of cross-supportproperty for eliminating patterns involving itemswith substantially different support levels. our experimentalresults demonstrate the effectiveness of this method forfinding patterns in dense data sets even at very low supportthresholds, where most of the existing algorithms wouldbreak down. finally, hyperclique patterns also show greatpromise for clustering items in high dimensional space.
clustering item data sets with association-taxonomy similarity. we explore in this paper the efficient clustering of itemdata. different from those of the traditional data, the featuresof item data are known to be of high dimensionalityand sparsity. in view of the features of item data, we devisein this paper a novel measurement, called the association-taxonomysimilarity, and utilize this measurement to performthe clustering. with this association-taxonomy similaritymeasurement, we develop an efficient clustering algorithm,called algorithm at (standing for association-taxonomy),for item data. two validation indexes basedon association and taxonomy properties are also devised toassess the quality of clustering for item data. as validatedby the real dataset, it is shown by our experimental resultsthat algorithm at devised in this paper significantly outperformsthe prior works in the clustering quality as measuredby the validation indexes, indicating the usefulness ofassociation-taxonomy similarity in item data clustering.
mixtures of arma models for model-based time series clustering. clustering problems are central to many knowledge discoveryand data mining tasks. however, most existing clusteringmethods can only work with fixed-dimensional representationsof data patterns. in this paper, we study the clusteringof data patterns that are represented as sequencesor time series possibly of different lengths. we propose amodel-based approach to this problem using mixtures of autoregressivemoving average (arma) models. we derive anexpectation-maximization (em) algorithm for learning themixing coefficients as well as the parameters of the componentmodels. experiments were conducted on simulatedand real datasets. results show that our method comparesfavorably with another method recently proposed by othersfor similar time series clustering problems.
cost-sensitive learning by cost-proportionate example weighting. we propose and evaluate a family of methods for convertingclassifier learning algorithms and classification theoryinto cost-sensitive algorithms and theory. the proposedconversion is based on cost-proportionate weighting of thetraining examples, which can be realized either by feedingthe weights to the classification algorithm (as often done inboosting), or by careful subsampling. we give some theoreticalperformance guarantees on the proposed methods,as well as empirical evidence that they are practical alternativesto existing approaches. in particular, we proposecosting, a method based on cost-proportionate rejectionsampling and ensemble aggregation, which achievesexcellent predictive performance on two publicly availabledatasets, while drastically reducing the computation requiredby other methods.
from path tree to frequent patterns: a framework for mining frequent patterns. in this paper, we propose a new framework for miningfrequent patterns from large transactional databases. thecore of the framework is of a novel coded prefix-path treewith two representations, namely, a memory-based prefix-pathtree and a disk-based prefix-path tree. the disk-basedprefix-path tree is simple in its data structure yet rich ininformation contained, and is small in size. the memory-basedprefix-path tree is simple and compact. upon thememory-based prefix-path tree, a new depth-first frequentpattern discovery algorithm, called p p-mine, is proposedin this paper that outperforms fp-growth significantly. thememory-based prefix-path tree can be stored on disk usinga disk-based prefix-path tree with assistance of the new codingscheme. we present efficient loading algorithms to loadthe minimal required disk-based prefix-path tree into mainmemory. our technique is to push constraints into the loadingprocess, which has not been well studied yet.
fast parallel association rule mining without candidacy generation. in this paper we introduce a new parallel algorithm mlfpt (multiple local frequent pattern tree) [11] for parallel mining of frequent patterns, based on fp-growth mining, that uses only two full i/o scans of the database, eliminating the need for generating the candidate items and distributing the work fairly among processors. we have devised partitioning strategies at different stages of the mining process to achieve near optimal balancing between processors.we have successfully tested our algorithm on datasets larger than 50 million transactions.
irc: an iterative reinforcement categorization algorithm for interrelated web objects. most existing categorization algorithms deal with homogeneous web data objects, and consider interrelated objects as additional features when taking the interrelationships withother types of objects into account. however, focusing on any single aspects of these interrelationships and objects will not fully reveal their true categories. in this paper, wepropose a novel categorization algorithm, the iterative reinforcement categorization algorithm (irc), to exploit the full interrelationships between the heterogeneous objects on the web.irc attempts to classify the interrelated web objects by iterative reinforcement between individual classification results of different types via the interrelationships. experiments on a clickthrough log dataset from msn search engine show that, with the f1 measures, irc achieves a 26.4% improvement over a pure content-based classification method, a 21% improvement over a query metadata-based method, and a 16.4% improvement over a virtual document-based method. furthermore, our experiments show that irc converges rapidly.
clustering spatial data when facing physical constraints. clustering spatial data is a well-known problem that hasbeen extensively studied to find hidden patterns or meaningfulsub-groups and has many applications such as satelliteimagery, geographic information systems, medical imageanalysis, etc. although many methods have been proposedin the literature, very few have considered constraintssuch that physical obstacles and bridges linking clustersmay have significant consequences on the effectiveness ofthe clustering. taking into account these constraints duringthe clustering process is costly, and the effective modeling ofthe constraints is of paramount importance for good performance.in this paper, we define the clustering problem in thepresence of constraints - obstacles and crossings - and investigateits efficiency and effectiveness for large databases.in addition, we introduce a new approach to model theseconstraints to prune the search space and reduce the numberof polygons to test during clustering. the algorithmdbcluc we present detects clusters of arbitrary shape andis insensitive to noise and the input order. its average runningcomplexity is o(nlogn) where n is the number of dataobjects.
discriminatively trained markov model for sequence classification. in this paper, we propose a discriminative counterpart of the directed markov models of order k - 1, or mm(k-1) for sequence classification. mm(k-1) models capture dependencies among neighboring elements of a sequence. the parameters of the classifiers are initialized to based on the maximum likelihood estimates for their generative counterparts. we derive gradient based update equations for the parameters of the sequence classifiers in order to maximize the conditional likelihood function. results of our experiments with data sets drawn from biological sequence classification (specifically protein function and subcellular localization) and text classification applications show that the discriminatively trained sequence classifiers outperform their generative counterparts, confirming the benefits of discriminative training when the primary objective is classification.our experiments also show that the discriminatively trained mm(k - 1) sequence classifiers are competitive with the computationally much more expensive support vector machines trained using k-gram representations of sequences.
cbc: clustering based text classification requiring minimal labeled data. semi-supervised learning methods construct classifiersusing both labeled and unlabeled training data samples.while unlabeled data samples can help to improve theaccuracy of trained models to certain extent, existingmethods still face difficulties when labeled data is notsufficient and biased against the underlying datadistribution. in this paper, we present a clustering basedclassification (cbc) approach. using this approach,training data, including both the labeled and unlabeleddata, is first clustered with the guidance of the labeleddata. some of unlabeled data samples are then labeledbased on the clusters obtained. discriminative classifierscan subsequently be trained with the expanded labeleddataset. the effectiveness of the proposed method isjustified analytically. our experimental resultsdemonstrated that cbc outperforms existing algorithmswhen the size of labeled dataset is very small.
gspan: graph-based substructure pattern mining. we investigate new approaches for frequent graph-basedpattern mining in graph datasets and propose a novel algorithmcalled gspan (graph-based substructure pattern mining),which discovers frequent substructures without candidategeneration. gspan builds a new lexicographic orderamong graphs, and maps each graph to a unique minimumdfs code as its canonical label. based on this lexico-graphicorder, gspan adopts the depth-first search strategyto mine frequent connected subgraphs efficiently. our performancestudy shows that gspan substantially outperformsprevious algorithms, sometimes by an order of magnitude.
a comparison of stacking with meta decision trees to bagging, boosting, and stacking with other methods. abstract. meta decision trees (mts) are a method for combining multiple classifiers. we present an integration of the algorithm mlc4.5 for learning mts into the weka data mining suite. we compare classifier ensembles combined with mdts to bagged and boosted decision trees, and to classifier ensembles combined with other methods: voting and stacking with three different meta-level classifiers (ordinary decision trees, naive bayes, and multi-response linear regression -mlr).
mining case bases for action recommendation. corporations and institutions are often interested inderiving marketing strategies from corporate data andproviding informed advice for their customers oremployees. for example, a financial institution mayderive marketing strategies for turning their reluctantcustomers into active ones and a telecommunicationscompany may plan actions to stop their valuablecustomers from leaving. in data mining terms, theseadvice and action plans are aimed at convertingindividuals from an undesirable class to a desirable one,or to help devising a direct-marketing plan in order toincrease the profit for the institution. in this paper, wepresent an approach to use role models' for generatingsuch advice and plans. these role models are typicalcases that form a case base and can be used forcustomer advice generation. for each new customerseeking advice, a nearest-neighbor algorithm is used tofind a cost-effective and highly probable plan forswitching a customer to the most desirable role models.in this paper, we explore the tradeoff among time, spaceand quality of computation in this case-based reasoningframework. we demonstrate the effectiveness of themethods through empirical results.
mining surveillance video for independent motion detection. this paper addresses the special applications of datamining techniques in homeland defense. the problemtargeted, which is frequently encountered in military/intelligence surveillance, is to mine a massive surveillancevideo database automatically collected to retrieve theshots containing independently moving targets. a novelsolution to this problem is presented in this paper, whichoffers a completely qualitative approach to solving for theautomatic independent motion detection problem directlyfrom the compressed surveillance video in a faster thanrealtime mining performance. this approach is based onthe linear system consistency analysis, and consequentlyis called qls. sincetheqls approach only focuses onwhat exactly is necessary to compute a solution, it savesthe computation to a minimum and achieves the efficacy tothe maximum. evaluations from real data show that qlsdelivers effective mining performance at the achieved efficiency.