waashk/dblp huggingface dataset:
efficiently mining maximal frequent itemsets. we present genmax, a backtrack search based algorithm for mining maximal frequent itemsets. genmax uses a number of optimizations to prune the search space.it usesa novel technique called progressive focusing to perform maximality checking, and diffset propagation to perform fast frequency computation. systematic experimental comparison with previous work indicates that different methods have varying strengths and weaknesses based on dataset characteristics. we found genmax to be a highly efficient method to mine the exact set of maximal patterns.
stability of feature selection algorithms. with the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected. this study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set. we assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset. we examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them. we perform a series of experiments with several feature selection algorithms on a set of proteomics datasets. the experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms. finally we show how stability profiles can support the choice of a feature selection algorithm.
mining frequent itemsets from secondary memory. mining frequent itemsets is at the core of mining association rules, and is by now quite well understood algorithmically for main memory databases. in this paper, we investigate approaches to mining frequent itemsets when the database or the data structures used in the mining are too large to fit in main memory. experimental results show that our techniques reduce the required disk accesses by orders of magnitude, and enable truly scalable data mining.
distance measures for effective clustering of arima time-series. many environmental and socioeconomic time-series data can be adequately modeled using auto-regressiveintegrated moving average (arima) models. we call such time-series arima time-series. we consider the problem of clustering arima time-series. we propose the use of the linear predictive coding (lpc) cepstrum of time-series for clustering arima time-series, by using the euclideandistance between the lpc cepstra of two time-series as their dissimilarity measure. we demonstrate that lpc cepstral coefficients have the desire features for accurate clustering and efficient indexing of arima time-series. for example, few lpc cepstral coefficients are sufficient in order todiscriminate between time-series that are modeled by different arima models. in fact this approach requires fewer coefficients than traditional approaches, such as dft and dwt. the proposed distance measure can be use for measuring the similarity between different arima models as well.we cluster arima time-series using the partition around medoids method with various similarity measures. we present experimental results demonstrating that using the proposed measure we achieve significantly betterclusterings of arima time-series data as compared to clusterings obtained by using other traditional similaritymeasures, such as dft, dwt, pca, etc. experiments wereperformed both on simulated as well as real data.
the diasdem framework for converting domain-specific texts into xml documents with data mining techniques. modern organizations are accumulating huge volumesof textual documents. to turn archives into valuable know-ledge sources, textual content must become explicit andqueryable. semantic tagging with markup languages suchas xml satisfies both requirements. we thus introduce thediasdem* framework for extra ting semantics from structural text units (e.g., sentences), assigning xml tags to them and deriving a flat xml dtd for the archive. diasdem focuses on archives characterized by a peculiar terminologyand by an implicit structure such as court filings and company reports. in the knowledge discovery phase, text units are iteratively clustered by similarity of their content. eachiteration outputs clusters satisfying a set of quality criteria.text units contained in these clusters are tagged with semi-automatically determined luster labels and xml tags respectively. additionally, extracted named entities (e.g.,per-sons) serve as attributes of xml tags. we apply the frame-work in a case study on the german commercial register.
learning from order examples. we advocate a new learning task that deals with ordersof items, and we call this the learning from order examples(loe) task. the aim of the task is to acquire the rule thatis used for estimating the proper order of a given unordereditem set. the rule is acquired from training examples thatare ordered item sets. we present several solution methodsfor this task, and evaluate the performance and the characteristicsof these methods based on the experimental resultsof tests using both artificial data and realistic data.
a bayesian framework for regularized svm parameter estimation. the support vector machine (svm) is considered here in the context of pattern classification. the emphasis is on the soft margin classifier which uses regularization to handle non-separable learning samples. we present an svm parameter estimation algorithm that first identifies a subset of the learning samples that we call the support set and then determines not only the weights of the classifier but also the hyperparameter that controls the influence of the regularizing penalty term on basis thereof. we provide numerical results using several data sets from the public domain.
filling-in missing objects in orders. filling-in techniques are important, since missing values frequently appear in real data. such techniques have been established for categorical or numerical values. though lists of ordered objects are widely used as representational forms (e.g., web search results, best-seller lists), filling-in techniques for orders have received little attention. we therefore propose a simple but effective technique to fill-in missing objects in orders. we built this technique into our collaborative filtering system.
an algebraic approach to data mining: some examples. in this paper, we introduce an algebraic approach tothe foundations of data mining. our approach is basedupon two algebras of functions defined over a commonstate space x and a pairing between them.one algebra is an algebra of state space observations, and the other is an algebra of labeled sets ofstates.we interpret h as the algebraic encoding of the dataand the pairing as the misclassification rate when theclassifer f is applied to the set of states x.in this paper, we give a realization theorem givingconditions on formal series of data sets built from dthat imply there is a realization involving a state spacex, a classifier f \in r and a set of labeled states x \in r_0that yield this series.
supervised ordering - an empirical survey. ordered lists of objects are widely used as representational forms. such ordered objects include web search results or bestseller lists. in spite of their importance, methods of processing orders have received little attention. however, research concerning orders has recently become common; in particular, researchers have developed various methods for the task of supervised ordering to acquire functions for object sorting from example orders. here, we give a unified view of these methods and our new one, and empirically survey their merits and demerits.
the rough set approach to association rule mining. in transaction processing, an association is said to existbetween two sets of items when a transaction containingone set is likely to also contain the other. in informationretrieval, an association between two sets of keywords occurswhen they co-occur in a document. similarly, in datamining, an association occurs when one attribute set occurstogether with another. as the number of such associationsmay be large, maximal association rules are sought, e.g.,feldman et al (1997, 1998).rough set theory is a successful tool for data mining. byusing this theory, rules similar to maximal associations canbe found. however, we show that the rough set approach todiscovering knowledge is much simpler than the maximalassociation method.
svm based models for predicting foreign currency exchange rates. support vector machine (svm) has appeared as a powerfultool for forecasting forex market and demonstrated betterperformance over other methods, e.g., neural network orarima based model. svm-based forecasting modelnecessitates the selection of appropriate kernel function andvalues of free parameters: regularization parameter and \varepsilon-insensitive loss function. in this paper, we investigate the effectof different kernel functions, namely, linear, polynomial, radialbasis and spline on prediction error measured by several widelyused performance metrics. the effect of regularizationparameter is also studied. the prediction of six different foreigncurrency exchange rates against australian dollar has beenperformed and analyzed. some interesting results are presented.
predicting distribution of a new forest disease using one-class svms. in california, a newly discovered virulent pathogen(phytophthora ramorum) has killed thousands of nativeoak trees. mapping the potential distribution of thepathogen is essential for decision makers to assess therisk of the pathogen and aid in preventing its furtherspread. most methods used to map potential ranges ofspecies (e.g. multivariate or logistic regression) requireboth presence and absence data, the latter of which is notalways feasibly collected. in this study, we present theone-class support vector machine (svm) to predict thepotential distribution of sudden oak death in california.the model was developed using presence data collectedthroughout the state, and tested for accuracy using a 5-fold cross-validation approach. the model performedwell, and provided 91% predicted accuracy. we believeone-class svm when coupled with geographicalinformation systems (gis) will become a very usefulmethod to deal with presence-only data in ecologicalanalysis over a range of scales.
categorization and keyword identification of unlabeled documents. in this paper we first propose a global unsupervised feature selection approach for text, based on frequent itemset mining. as a result, each document is represented as a set of words that co-occur frequently in the given corpus of documents. we then introduce a locally adaptive clustering algorithm, designed to estimate (local) word relevance and, simultaneously, to group the documents. we present experimental results to demonstrate the feasibility of our approach. furthermore, the analysis of the weights credited to terms provides evidence that the identified keywords can guide the process of label assignment to clusters. we take into consideration both spam email filtering and general classification datasets. our analysis of the distribution of weights in the two cases provides insights on how the spam problem distinguishes from the general classification case.
understanding helicoverpa armigera pest population dynamics related to chickpea crop using neural networks. insect pests are a major cause of crop loss globally. pestmanagement will be effective and efficient if we canpredict the occurrence of peak activities of a given pest.research efforts are going on to understand the pestdynamics by applying analytical and other techniques onpest surveillance data sets. in this study we make an effortto understand pest population dynamics using neuralnetworks by analyzing pest surveillance data set ofhelicoverpa armigera or pod borer on chickpea (cicerarietinum l.) crop. the results show that neural networkmethod successfully predicts the pest attack incidences forone week in advance.
generation of attribute value taxonomies from data for data-driven construction of accurate and compact classifiers. attribute value taxonomies (avt) have been shown to be useful in constructing compact, robust, and comprehensible classifiers. however, in many application domains, human-designed avts are unavailable. we introduce avt-learner, an algorithm for automated construction of attribute value taxonomies from data. avt-learner uses hierarchical agglomerative clustering (hac) to cluster attribute values based on the distribution of classes that co-occur with the values. we describe experiments on uci data sets that compare the performance of avt-nbl (an avt-guided naive bayes learner) with that of the standard naive bayes learner (nbl) applied to the original data set. our results show that the avts generated by avt-learner are competitive with human-generated avts (in cases where such avts are available). avt-nbl using avts generated by avt-learner achieves classification accuracies that are comparable to or higher than those obtained by nbl; and the resulting classifiers are significantly more compact than those generated by nbl.
a scalable algorithm for clustering sequential data. in recent years, we have seen an enormous growth in the amount of available commercial and scientific data. data from domains such as protein sequences, retail transactions, intrusion detection, and web-logs have an inherent sequential nature. clustering of such data sets is usefulfor various purposes. for example, clustering of sequences from commercialdata sets may help marketer identify different customer groups based upon their purchasing patterns. grouping protein sequences that share similar structure helps in identifying sequences with similar functionality. over the years, many methods have been developed for clustering objects according to their similarity. however these methods tend to have a computational complexity that is at least quadratic on the number of sequences. in this paperwe present an entirely different approach to sequence clustering that does not require an all-against-all analysis and uses a nearlinear complexity k-means based clustering algorithm. our experiments using data sets derived from sequences of purchasing transactions and protein sequences show that this approach is scalable and leads to reasonably good clusters.
semi-supervised mixture-of-experts classification. we introduce a mixture-of-experts technique that is a generalization of mixture modeling techniques previously suggested for semi-supervised learning. we apply the bias-variance decomposition to semi-supervised classification and use the decomposition to study the effects from adding unlabeled data when learning a mixture model. our empirical results indicate that the biggest gain from adding unlabeled data comes from the reduction of the model variance, whereas the behavior of the bias error term heavily depends on the correctness of the underlying model assumptions.
reliable detection of episodes in event sequences. suppose one wants to detect "bad" or "suspicious" subsequencesin event sequences.whether an observed patternof activity (in the form of a particular subsequence) is significantand should be a cause for alarm, depends on howlikely it is to occur fortuitously.a long enough sequenceof observed events will almost certainly contain any subsequence,and setting thresholds for alarm is an important issuein a monitoring system that seeks to avoid false alarms.suppose a long sequence t of observed events contains asuspicious subsequence pattern s within it, where the suspicioussubsequence s consists of m events and spans a windowof size w within t.we address the fundamental problem:is a certain number of occurrences of a particular subsequenceunlikely to be fortuitous (i.e., indicative of suspiciousactivity)?if the probability of fortuitous occurrencesis high and an automated monitoring system flags it as suspiciousanyway, then such a system will suffer from generatingtoo many false alarms.this paper quantifies the probabilityof such an s occuring in t within a window of sizew, the number of distinct windows containing s as a subsequence,the expected number of such occurrences, its variance,and establishes its limiting distribution that allows toset up an alarm threshold so that the probability of falsealarms is very small.we report on experiments confirmingthe theory and showing that we can detect bad subsequenceswith low false alarm rate.
orthogonal decision trees. this paper introduces orthogonal decision trees that offer an effective way to construct a redundancy-free, accurate, and meaningful representation of large decision-tree-ensembles often created by popular techniques such as bagging, boosting, random forests and many distributed and data stream mining algorithms. orthogonal decision trees are functionally orthogonal to each other and they correspond to the principal components of the underlying function space. this paper offers a technique to construct such trees based on eigen-analysis of the ensemble and offers experimental results to document the performance of orthogonal trees on grounds of accuracy and model complexity.
unimodal segmentation of sequences. we study the problem of segmenting a sequence into k pieces so that the resulting segmentation satisfies monotonicity or unimodality constraints. unimodal functions can be used to model phenomena in which a measured variable first increases to a certain level and then decreases. we combine a well-known unimodal regression algorithm with a simple dynamic-programming approach to obtain an optimal quadratic-time algorithm for the problem of unimodal k-segmentation. in addition, we describe a more efficient greedy-merging heuristic that is experimentally shown to give solutions very close to the optimal. as a concrete application of our algorithms, we describe two methods for testing if a sequence behaves unimodally or not. our experimental evaluation shows that our algorithms and the proposed unimodality tests give very intuitive results.
on the privacy preserving properties of random data perturbation techniques. privacy is becoming an increasingly important issue inmany data mining applications. this has triggered the developmentof many privacy-preserving data mining techniques.a large fraction of them use randomized data distortiontechniques to mask the data for preserving the privacyof sensitive data. this methodology attempts to hidethe sensitive data by randomly modifying the data values oftenusing additive noise. this paper questions the utility ofthe random value distortion technique in privacy preservation.the paper notes that random objects (particularly randommatrices) have "predictable" structures in the spectraldomain and it develops a random matrix-based spectral filteringtechnique to retrieve original data from the datasetdistorted by adding random values. the paper presents thetheoretical foundation of this filtering method and extensiveexperimental results to demonstrate that in many cases randomdata distortion preserve very little data privacy. thepaper also points out possible avenues for the developmentof new privacy-preserving data mining techniques like exploitingmultiplicative and colored noise for preserving privacyin data mining applications.
a framework for semi-supervised learning based on subjective and objective clustering criteria. in this paper, we propose a semi-supervised framework for learning a weighted euclidean subspace, where the best clustering can be achieved. our approach capitalizes on user-constraints and the quality of intermediate clustering results in terms of its structural properties. it uses the clustering algorithm and the validity measure as parameters.
mining decision trees from data streams in a mobile environment. this paper presents a novel fourier analysis-based technique toaggregate, communicate, and visualize decision trees in a mobile environment. fourier representation of a decision tree has several useful properties that are particularly useful for mining continuous data streams from small mobile computing devices. this paper presents algorithms to compute the fourier spectrum of a decision tree and the vice versa. it offers a framework to aggregate decision trees in their fourier representations. it a so describes atouch-pad/ticker-based approach to visualize decision trees using their fourier spectrum and an implementation for pdas..
clustering validity assessment: finding the optimal partitioning of a data set. clustering s a mostly unsupervised procedure and the majority of the clustering algorithms depend on certain assumptions in order to define the subgroups present in a data set. as a consequence, in most applications the resulting clustering scheme requires some sort ofevaluation as regards its validity. in this paper we present a clustering validity procedure,which evaluates the results of clustering algorithms on data sets. we define a validity index, s_dbw, based on well-defined clustering criteria enabling the selection of the optimal input parameters' values for a clustering algorithm that result in the best partitioning of a data set.we evaluate the reliability of our index both theoretically and experimentally, considering three representative clustering algorithms ran on synthetic and real data sets. also, we carried out an evaluation study to compare s_dbw performance with other known validity indices.our approach performed favorably in all cases, even in those that other indices failed to indicate the correct partitions in a data set.
gradual model generator for single-pass clustering. we present an algorithm for generating a mixture model from a data set by converting the data into a model. the method is applicable when only part of the data fits in the main memory at the same time. the generated model is a gaussian mixture model but the algorithm can be adapted to other types of models, too. the user cannot specify the size of the generated model. we also introduce a post-processing method, which can reduce the size of the model without using the original data. this will result in a more compact model with fewer components, but with approximately the same representation accuracy as the original model. our comparisons show that the algorithm produces good results and is quite efficient. the whole process requires only 0.5-10% of the time spent by the expectation-maximization algorithm.
comparing pure parallel ensemble creation techniques against bagging. we experimentally evaluate randomization-based approachesto creating an ensemble of decision-tree classifiers.unlike methods related to boosting, all of the eightapproaches considered here create each classifier in an ensembleindependently of the other classifiers. experimentswere performed on 28 publicly available datasets, usingc4.5 release 8 as the base classifier. while each of the otherseven approaches has some strengths, we find that none ofthem is consistently more accurate than standard baggingwhen tested for statistical significance.
facilitating fuzzy association rules mining by using multi-objective genetic algorithms for automated clustering. in this paper, we propose an automated clustering methodbased on multi-objective genetic algorithms (ga); the aim ofthis method is to automatically cluster values of a givenquantitative attribute to obtain large number of largeitemsets in low duration (time). we compare the proposedmulti-objective ga-based approach with cure-basedapproach. in addition to the autonomous specification offuzzy sets, experimental results showed that the proposedautomated clustering exhibits good performance overcure-based approach in terms of runtime as well as thenumber of large itemsets and interesting association rules.