waashk/dblp huggingface dataset:
multivariate supervised discretization, a neighborhood graph approach. we present a new discretization method in the contextof supervised learning. this method entitled hyperclusterfinder is characterized by its supervised and polytheticbehavior. the method is based on the notion of clustersand processes in two steps. first, a neighborhood graphconstruction from the learning database allows discoveringhomogenous clusters. second, the minimal and maximalvalues of each cluster are transferred to each dimension inorder to define some boundaries to cut the continuous attributein a set of intervals. the discretization abilities ofthis method are illustrated by some examples, in particular,processing the xor problem.
exploring interestingness through clustering: a framework. determining interestingness is a notoriously difficultproblem: it is subjective and elusive to capture. it is alsobecoming an increasingly more important problem in kddas the number of mined patterns increases. in this work weintroduce and investigate a framework for association ruleclustering that enables automating much of the laboriousmanual effort normally involved in the exploration and understandingof interestingness. clustering is ideally suitedfor this task; it is the unsupervised organization of patternsinto groups, so that patterns in the same group are moresimilar to each other than to patterns in other groups. wealso define a data-driven inferred labeling of these clusters,the ancestor coverage, which provides an intuitive, conciserepresentation of the clusters.
automatically mining result records from search engine response pages. usually, web applications such as deep web crawlers, metasearch engines, and other web mining systems need to extract information displayed in the form of result records on response pages returned by search engines in response to submitted queries. extracting such records is challenging as search engines are heterogeneous in displaying their records. in addition, response pages returned by many search engines include other noisy content such as advertisements, suggestion links, etc., which make the extraction task even more complicated. in this paper, we propose a highly effective and efficient algorithm for automatically mining result records from search engine response pages.
on incorporating subjective interestingness into the mining process. subjective interestingness is at the heart of thesuccessful discovery of association rules. to determine what is subjectively interesting, users' domainknowledge must be applied. [7] introduced an approach that requires very little domain knowledgeand inter action to eliminate the majority of therules that are subjectively not interesting. in thispaper we investigate how this approach can be incorporated into the mining process, the benefits anddisadvantages of doing so, and examine the resultsof its application to real databases.
complex spatial relationships. this paper describes the need for mining complex relationshipsin spatial data. complex relationships are definedas those involving two or more of: multi-feature colocation,self-colocation, one-to-many relationships, self-exclusionand multi-feature exclusion. we demonstrate that even inthe mining of simple relationships, knowledge of complexrelationships is necessary to accurately calculate the significanceof results. we implement a representation of spatialdata such that it contains known 'weak-monotonic' properties,which are exploited for the efficient mining of complexrelationships, and discuss the strengths and limitations ofthis representation.
data analysis and mining in ordered information tables. many real world problems deal with ordering objects instead of classifying objects, although majority of research in machine learning and data mining has been focused on the latter. for modeling ordering problems, we generalize the notion of information tables to ordered information tables by adding order relations on attribute values. the problem of mining ordering rules is formulated as findingassociation between orderings of attribute values and the overall ordering of objects. an ordering rules ay state that "if the value of an object x on an attribute a is ordered ahead of the value of another object y on the same attribute, then x is ordered ahead of y" for mining ordering rules, we first transform an ordered information table into a binaryinformation, and then apply any standard machine learning and data mining algorithms. as an illustration, we analyze in detail maclean's universities ranking for the year 2000.
the eq framework for learning equivalence classes of bayesian networks. this paper proposes a theoretical and an algorithmic framework for the analysis and the design of efficient learning algorithms which explore the space of equivalence classes of bayesian network structures.this framework is composed of a generic learning model which uses essential graphs and more general partially directed graphs i order to represent the equivalence classes evaluated during search, operational characterizations of these graphs, processing procedures and formulas for directly calculating their score.the experimental results of the algorithms designed within this framework show that the space of equivalence classes may be explored efficiently and with better results than the classical search in the space of bayesian network structures.
mining constrained association rules to predict heart disease. this work describes our experiences on discovering association rules in medical data to predict heart disease. we focus on two aspects in this work: mapping medical data toa transaction format suitable for mining association rules and identifying useful constraints. based on these aspects we introduce an improved algorithm to discover constrainedassociation rules. we present an experimental sectionexplaining several interesting discovered rules.
bayesian data mining on the web with b-course. b-course is a free 1 web-based bayesian data mining service. this service allows the users to analyze their own data for multivariate probabilistic dependencies represented as bayesian network models. in addition to this, b-course also offers facilities for inferring certain type of causal dependencies from the data. the software is especially suitable for educational purposes as the tutorial style user-friendly interface intertwines the steps in the data analysiswith support material that gives an informal introduction to the bayesian approach adopted. nevertheless, although the analysis methods, modeling assumptions and restrictionsare totally transparent to the user, this transparency is not achieved at the expense of analysis power: with the restrictions stated in the support material, b-course is a powerful analysis tool exploiting several theoretically elaborate results developed recently in the fields of bayesian and causal modeling.
evaluating attraction in spatial point patterns with an application in the field of cultural history. spatial collocation rules are often useful for describing dependencies between spatial features. still, the commonly used criteria for the interestingness of the rules and the selected neighbourhood constraints for spatial objects may be too rough for capturing the essentials of such dependencies. we demonstrate the difficulties with concrete examples on a large place-name data set. we propose a technique based on simple density estimation for assessing the interestingness with different neighbouring constraints.
tecno-streams: tracking evolving clusters in noisy data streams with a scalable immune system learning model. artificial immune system (ais) models hold many promises inthe field of unsupervised learning. however, existing models arenot scalable, which makes them of limited use in data mining. wepropose a new ais based clustering approach (tecno-streams)that addresses the weaknesses of current ais models. comparedto existing ais based techniques, our approach exhibits superiorlearning abilities, while at the same time, requiring low memoryand computational costs. like the natural immune system, thestrongest advantage of immune based learning compared to otherapproaches is expected to be its ease of adaptation to the dynamicenvironment that characterizes several applications, particularlyin mining data streams. we illustrate the ability of the proposedapproach in detecting clusters in noisy data sets, and in miningevolving user profiles from web clickstream data in a single pass.tecno-streams adheres to all the requirements of clusteringdata streams: compactness of representation, fast incremental processingof new data points, and clear and fast identification of outliers.
a clustering method for very large mixed data sets. in the developed countries, especially over the last decade, there has been an explosive growth in the capability to generate, collect and use very large data sets. the objects of these data sets could be simultaneously described by quantitative and qualitative attributes. at present, algorithms able to process either very large data sets (in metric spaces) or mixed(qualitative and quantitative) incomplete data (missing value) sets have been developed, but not for very large mixed incomplete data sets. in this paper we introduce a new clustering method named glc+to process very large mixed incomplete data sets in order to obtain apartition in connected sets.
dependency networks for relational data. instance independence is a critical assumption of traditional machine learning methods contradicted by many relational datasets. for example, in scientific literature datasets there are dependencies among the references of a paper. recent work on graphical models for relational data has demonstrated significant performance gains for models that exploit the dependencies among instances. in this paper, we present relational dependency networks (rdns), a new form of graphical model capable of reasoning with such dependencies in a relational setting. we describe the details of rdn models and outline their strengths, most notably the ability to learn and reason with cyclic relational dependencies. we present rdn models learned on a number of real-world datasets, and evaluate the models in a classification context, showing significant performance improvements. in addition, we use synthetic data to evaluate the quality of model learning and inference procedures.
leveraging relational autocorrelation with latent group models. the presence of autocorrelation provides a strong motivation for using relational learning and inference techniques. autocorrelation is a statistical dependence between the values of the same variable on related entities and is a nearly ubiquitous characteristic of relational data sets. recent research has explored the use of collective inference techniques to exploit this phenomenon. these techniques achieve significant performance gains by modeling observed correlations among class labels of related instances, but the models fail to capture a frequent cause of autocorrelation - the presence of underlying groups that influence the attributes on a set of entities. we propose a latent group model (lgm) for relational data, which discovers and exploits the hidden structures responsible for the observed autocorrelation among class labels. modeling the latent group structure improves model performance, increases inference efficiency, and enhances our understanding of the datasets. we evaluate performance on three relational classification tasks and show that lgm outperforms models that ignore latent group structure, particularly when there is little information with which to seed inference.
compound classification models for recommender systems. recommender systems recommend products to customers based on ratings or past customer behavior. without any information about attributes of the products or customers involved, the problem has been tackled most successfully by a nearest neighbor method called collaborative filtering in the context, while additional efforts invested in building classification models did not pay off and did not increase the quality. therefore, classification methods have mainly been used in conjunction with product or customer attributes. starting from a view on the plain recommendation task without attributes as a multi-class classification problem, we investigate two particularities, its autocorrelation structure as well as the absence of re-occurring items (repeat buying). we adapt the standard generic reductions 1-vs-rest and 1-vs-1 of multi-class problems to a set of binary classification problems to these particularities and thereby provide a generic compound classifier for recommender systems. we evaluate a particular specialization thereof using linear support vector machines as member classifiers on movielens data and show that it outperforms state-of-the-artmethods, i.e., item-based collaborative filtering.
simple estimators for relational bayesian classifiers. in this paper we present the relational bayesianclassifier (rbc), a modification of the simple bayesianclassifier (sbc) for relational data. there exist severalbayesian classifiers that learn predictive models ofrelational data, but each uses a different estimationtechnique for modeling heterogeneous sets of attributevalues. the effects of data characteristics on estimationhave not been explored. we consider four simpleestimation techniques and evaluate them on three real-worlddata sets. the estimator that assumes each multisetvalue is independently drawn from the same distribution(indepval) achieves the best empirical results. weexamine bias and variance tradeoffs over a range of datasets and show that indepval's ability to model moremultiset information results in lower bias estimates andcontributes to its superior performance.
on the tractability of rule discovery from distributed data. this paper analyses the tractability of rule selection for supervised learning in distributed scenarios. the selection of rules is usually guided by a utility measure such as predictive accuracy or weighted relative accuracy. a common strategy to tackle rule selection from distributed data is to evaluate rules locally on each dataset. while this works well for homogeneously distributed data, this work proves limitations of this strategy if distributions are allowed to deviate. the identification of those subsets for which local and global distributions deviate, poses a learning task of its own, which is shown to be at least as complex as discovering the globally best rules from local data.
mining association rules from stars. association rule mining is an important data mining problem.it is found to be useful for conventional relational data.however, previous work had mostly targeted on mining a single table.in real life, a database is typically made up of multiple table and one important case is where some of the tables form a star schema.that tables typically correspond to entity sets and joining the tables in a star schema gives relationship amoung entity sets which can be very interesting information.hence mining on the join result is an important problem.based on characteristics of the star schema we propose an efficient algorithm for mining association rules on the joinresult but without actually performing the join opertation.we show that this approach can significantly out-perform the join-then-mine approach even when the latter adopts a fastest known mining algorithm.
a high-performance distributed algorithm for mining association rules. we present a new distributed association rule mining(d-arm) algorithm that demonstrates superlinear speedupwith the number of computing nodes. the algorithm isthe first d-arm algorithm to perform a single scan overthe database. as such, its performance is unmatched byany previous algorithm. scale-up experiments over standard synthetic benchmarks demonstrate stable run time regardless of the number of computers. theoretical analysisreveals a tighter bound on error probability than the oneshown in the corresponding sequential algorithm.
visualizing association mining results through hierarchical clusters. we propose a new methodology for visualizing association mining results. inter-item distances are computed from combinations of item set supports. the new distances retain a simple pairwise structure, and are consistent with important frequently occurring item sets. thus standard tools of visualization, e.g. hierarchical clustering dendrograms can still be applied, while the distance information upon which they are based is richer. our approach is applicable to general association mining applications, as well as applications involving information spaces modeled by directed graphs, e.g. the web. in the context of collections of hypertext documents, the inter-document distances capture the information inherent in a collection's link structure, a for of link mining. we demonstrate our methodology with document sets extracted fro the science citation index, applying a metric that measures consistency between clusters and frequent itemsets.
impact studies and sensitivity analysis in medical data mining with roc-based genetic learning. roc curves have been used for a fair comparison of machinelearning algorithms since the late 90's. accordingly,the area under the roc curve (auc) is nowadays considereda relevant learning criterion, accommodating imbalanceddata, misclassification costs and noisy data.this paper shows how a genetic algorithm-based optimizationof the auc criterion can be exploited for impactstudies and sensitivity analysis.the approach is illustrated on the atherosclerosis identificationproblem, pkdd 2002 challenge.
an experimental comparison of supervised and unsupervised approaches to text summarization. the paper presents a direct comparison of supervised and unsupervised approaches to text summarization. as a representative supervised method, we use the c4.5 decision tree algorithm, extended with the minimum description length principle (mdl), and compare it against several unsupervised methods. it is found that a particular un-supervised method based on an extension of the k-means clustering algorithm, performs equal to and in some cases superior to the decision tree based method.
exploring the parameter state space of stacking. ensemble learning schemes are a new field in data mining.while current research concentrates mainly on improvingthe performance of single learning algorithms, an alternativeis to combine learners with different biases. stackingis the best-known such scheme which tries to combine learners'predictions or confidences via another learning algorithm.however, the adoption of stacking into the data mining communityis hampered by its large parameter space, consistingmainly of other learning algorithms: (1) the set of learning algorithmsto combine, (2) the meta-learner responsible for thecombining and (3) the type of meta-data to use: confidencesor predictions. none of these parameters are obvious choices.furthermore, little is known about the relation between parametersettings and performance of stacking. by exploring all ofstacking's parameter settings and their interdependencies, weintend make stacking a suitable choice for mainstream datamining applications.
efficient data mining for maximal frequent subtrees. a new type of tree mining is defined in this paper,which uncovers maximal frequent induced subtrees from adatabase of unordered labeled trees. a novel algorithm,pathjoin, is proposed. the algorithm uses a compact datastructure, fst-forest, which compresses the trees and stillkeeps the original tree structure. pathjoin generates candidatesubtrees by joining the frequent paths in fst-forest.such candidate subtree generation is localized and thussubstantially reduces the number of candidate subtrees. experimentswith synthetic data sets show that the algorithmis effective and efficient.
evaluating the utility of statistical phrases and latent semantic indexing for text classification. the term-based vector space model is a prominenttechnique to retrieve textual information. in this paper weexamine the usefulness of phrases as terms in vector-baseddocument classification. we focus on statistical techniquesto extract both adjacent and window phrases fromdocuments. we discover that the positive effect of addingphrase terms is very limited, if we have already achievedgood performance using single-word terms, even whensvd/lsi is used as dimensionality reduction method.
cluster cores-based clustering for high dimensional data. we propose a new approach to clustering high dimensional data based on a novel notion of cluster cores, instead of on nearest neighbors. a cluster core is a fairly dense group with a maximal number of pairwise similar objects. it represents the core of a cluster, as all objects in a cluster are with a great degree attracted to it. as a result, building clusters from cluster cores achieves high accuracy. other major characteristics of the approach include: (1) it uses a semantics-based similarity measure. (2) it does not incur the curse of dimensionality and is scalable linearly with the dimensionality of data. (3) it outperforms the well-known clustering algorithm, rock, with both lower time complexity and higher accuracy.
objective-oriented utility-based association mining. the necessity to develop methods for discovering associationpatterns to increase business utility of an enterprisehas long been recognized in data mining community.this requires modeling specific association patterns thatare both statistically (based on support and confidence) andsemantically (based on objective utility) relating to a givenobjective that a user wants to achieve or is interested in.however, we notice that no such a general model has beenreported in the literature. traditional association miningfocuses on deriving correlations among a set of items andtheir association rules like diaper ¿ beer only tell us thata pattern like fdiaperg is statistically related to an itemlike beer. in this paper, we present a new approach, calledobjective-oriented utility-based association (ooa)mining,to modeling such association patterns that are explicitlyrelating to a user's objective and its utility. due to its focuson a user's objective and the use of objective utility as keysemantic information to measure the usefulness of associationpatterns, ooa mining differs significantly from existingapproaches such as the existing constraint-based associationmining. we formally define ooa mining and developan algorithm for mining ooa rules. the algorithm is anenhancement to apriori with specific mechanisms for handlingobjective utility. we prove that the utility constraint isneither monotone nor anti-monotone nor succinct nor convertibleand present a novel pruning strategy based on theutility constraint to improve the efficiency of ooa mining.
face recognition using landmark-based bidimensional regression. this paper studies how biologically meaningful landmarks extracted from face images can be exploited for face recognition using the bidimensional regression. incorporating the correlation statistics of landmarks, this paper also proposes a new approach called eigenvalue weighted bidimensional regression. complex principal component analysis is used for computing eigenvalues and removing correlation among landmarks. we evaluate our approach using two standard face databases: the purdue ar and the nist feret. experimental results show that the bidimensional regression is an efficient method to exploit geometry information of face images.
k-d decision tree: an accelerated and memory efficient nearest neighbor classifier. most nearest neighbor (nn) classifiers employ nn searchalgorithms for the acceleration. however, nnclassification does not always require the nn search.based on this idea, we propose a novel algorithm namedk-d decision tree (kddt). since kddt uses voronoicondensed prototypes, it is less memory consuming thannaive nn classifiers. we have confirmed that kddt ismuch faster than nn search based classifiers through thecomparative experiment (from 9 to 369 times faster).