waashk/dblp huggingface dataset:
: using constrained models for guaranteed-error semantic compression. while a variety of lossy compression schemes have been developed for certain forms of digital data (e.g., images, audio, video), the area of lossy compression techniques for arbitrary data tables has been left relatively unexplored. nevertheless, such techniques are clearly motivated by the ever-increasing data collection rates of modern enterprises and the need for effective, guaranteed-quality approximate answers to queries over massive relational data sets.in this paper, we propose spartan, a system that takes advantage of attribute semantics and data-mining models to perform lossy compression of massive data tables. spartan is based on the novel idea of exploiting predictive data correlations and prescribed error-tolerance constraints for individual attributes to construct concise and accurate classification and regression tree (cart) models for entire columns of a table. more precisely, spartan selects a certain subset of attributes (referred to as predicted attributes) for which no values are explicitly stored in the compressed table; instead, concise error-constrained carts that predict these values (within the prescribed error tolerances) are maintained. to restrict the huge search space of possible cart predictors, spartan uses a bayesian network structure to guide the selection of cart models that minimize the overall storage requirement, based on the prediction and materialization costs for each attribute. spartan's cart-building algorithms employ novel integrated pruning strategies that take advantage of the given error constraints on individual attributes to minimize the computational effort involved. our experimentation with several real-life data sets offers convincing evidence of the effectiveness of spartan's model-based approach --- spartan is able to consistently yield substantially better compression ratios than existing semantic or syntactic compression tools (e.g., gzip) while utilizing only small samples of the data for model inference.
automatic scientific text classification using local patterns: kdd cup 2002 (task 1). in this paper, we describe our approach for addressing task 1 in the kdd cup 2002 competition. the approach is based on developing and using an improved automatic feature selection method in conjunction with traditional classifiers. the feature selection method used is based on capturing frequently occurring keyword combinations (or motifs) within short segments of the text of a document and has proved to produce more accurate classification results than approaches relying solely on using keyword-based features.
machine learning methods applied to dna microarray data can improve the diagnosis of cancer. the morbidity rate of cancer victims varies greatly for similar patients who receive similar treatments. it is hypothesized that this variation can be explained by the genetic heterogeneity of the disease. dna microarrays, which can simultaneously measure the expression level of thousands of different genes, have been successfully used to identify such genetic differences. however, microarray data typically has a large number of features and relatively few observations, meaning that conventional machine learning tools can fail when applied to such data. we describe a novel procedure called "nearest shrunken centroids" that has successfully detected clinically relevant genetic differences in cancer patients. this procedure has the potential to become a powerful tool for diagnosing and treating cancer.
meta-clustering of gene expression data and literature-based information. the current tendency in the life sciences to spawn ever growing amounts of high-throughput assays has led to a situation where the interpretation of data and the formulation of hypotheses lag the pace at which information is produced. although the first generation of statistical algorithms scrutinizing single, large-scale data sets found their way into the biological community, the great challenge to connect their results to existing knowledge still remains. despite the fairly large number of biological databases that is currently available, a lot of relevant information is found in free-text format (such as textual annotations, scientific abstracts and full publications). in this paper we explore how an integrated analysis of expression data and literature-extracted information can reveal biologically meaningful clusters not identified when using microarray information alone. the joint analysis is validated in terms of transcriptional regulation.
requirements for clustering data streams. scientific and industrial examples of data streams abound in astronomy, telecommunication operations, banking and stock-market applications, e-commerce and other fields. a challenge imposed by continuously arriving data streams is to analyze them and to modify the models that explain them as new data arrives. in this paper, we analyze the requirements needed for clustering data streams. we review some of the latest algorithms in the literature and assess if they meet these requirements.
instance filtering for entity recognition. in this paper we propose instance filtering as preprocessing step for supervised classification-based learning systems for entity recognition. the goal of instance filtering is to reduce both the skewed class distribution and the data set size by eliminating negative instances, while preserving positive ones as much as possible. this process is performed on both the training and test set, with the effect of reducing the learning and classification time, while maintaining or improving the prediction accuracy. we performed a comparative study on a class of instance filtering techniques, called stop word filters, that simply remove all the tokens belonging to a list of stop words. we evaluated our approach on three different entity recognition tasks (i.e. named entity, bio-entity and temporal expression recognition) in english and dutch, showing that both the skewness and the data set size are drastically reduced. consequently, we reported an impressive reduction of the computation time required for training and classification, while maintaining (and sometimes improving) the prediction accuracy.
a survey of data mining and knowledge discovery software tools. knowledge discovery in databases is a rapidly growing field, whose development is driven by strong research interests as well as urgent practical, social, and economical needs. while the last few years knowledge discovery tools have been used mainly in research environments, sophisticated software products are now rapidly emerging. in this paper, we provide an overview of common knowledge discovery tasks and approaches to solve these tasks. we propose a feature classification scheme that can be used to study knowledge and data mining software. this scheme is based on the software's general characteristics, database connectivity, and data mining characteristics. we then apply our feature classification scheme to investigate 43 software products, which are either research prototypes or commercially available. finally, we specify features that we consider important for knowledge discovery software to possess in order to accommodate its users effectively, as well as issues that are either not addressed or insufficiently solved yet.
a study of the behavior of several methods for balancing machine learning training data. there are several aspects that might influence the performance achieved by existing learning systems. it has been reported that one of these aspects is related to class imbalance in which examples in training data belonging to one class heavily outnumber the examples in the other class. in this situation, which is found in real world data describing an infrequent but important event, the learning system may have difficulties to learn the concept related to the minority class. in this work we perform a broad experimental evaluation involving ten methods, three of them proposed by the authors, to deal with the class imbalance problem in thirteen uci data sets. our experiments provide evidence that class imbalance does not systematically hinder the performance of learning systems. in fact, the problem seems to be related to learning with too few minority class examples in the presence of other complicating factors, such as class overlapping. two of our proposed methods deal with these conditions directly, allying a known over-sampling method with data cleaning methods in order to produce better-defined class clusters. our comparative experiments show that, in general, over-sampling methods provide more accurate results than under-sampling methods considering the area under the roc curve (auc). this result seems to contradict results previously published in the literature. two of our proposed methods, smote + tomek and smote + enn, presented very good results for data sets with a small number of positive examples. moreover, random over-sampling, a very simple over-sampling method, is very competitive to more complex over-sampling methods. since the over-sampling methods provided very good performance results, we also measured the syntactic complexity of the decision trees induced from over-sampled data. our results show that these trees are usually more complex then the ones induced from original data. random over-sampling usually produced the smallest increase in the mean number of induced rules and smote + enn the smallest increase in the mean number of conditions per rule, when compared among the investigated over-sampling methods.
open source data mining: workshop report. over the past decade tremendous progress has been made in data mining methods like clustering, classification, frequent pattern mining, and so on. unfortunately, however, the advanced implementations are often not made publicly available, and thus the results cannot be independently verified. we believe that this hampers the rapid advances in the field. with this workshop we intended to promote open source data mining (osdm) by creating a first meeting place to discuss open source data mining methods.
interface '99: a data mining overview. this personal overview of interface '99 is intended to communicate its meaning and relevance to sigkdd, as well as provide valuable information on trends within the interface for data miners seeking to learn more about statistics. in addition, it is the newest link in a bridge between the interface and kdd begun by references 2-4 and the sessions on kdd at interface '98 and interface '99.
multi-relational data mining 2005: workshop report. in this report we briefly review the 4th workshop on multi-relational data mining (mrdm-2005), which was organized by the authors and held in chicago, il, on august 21, as part of the workshop program of the eleventh acm sigkdd international conference on knowledge discovery and data mining. the goal of the workshop was to bring together researchers and practitioners of data mining interested in methods and applications of finding patterns in expressive languages from multi-relational, complex, and/or structured data.
kdd-2003 workshop on data mining standards, services and platforms (dm-ssp 03). at kdd 2003 a half day workshop was held on data mining standards and data mining services based on them. a theme of the workshop was that data mining standards have matured sufficiently that standards-based services and applications can now be deployed easily.
scalability and efficiency in multi-relational data mining. efficiency and scalability have always been important concerns in the field of data mining, and are even more so in the multi-relational context, which is inherently more complex. the issue has been receiving an increasing amount of attention during the last few years, and quite a number of theoretical results, algorithms and implementations have been presented that explicitly aim at improving the efficiency and scalability of multi-relational data mining approaches. with this article we attempt to present a structured overview.
data mining standards, services, and platforms 2004 (dm-ssp 2004). this is a summary of the workshop on data mining standards, services and platforms, which was held at kdd 2004. the workshop contained talks on the predictive model markup language (pmml) and the java standard for data mining jsr-73. it also contained talks on emerging service-based architectures for data mining. finally, infrastructures for privacy preserving data mining were also discussed.
mining sales data using a neural network model of market response. modeling aggregate market response is a core issue in marketing research. in this research, we extend previous forecasting comparative research by comparing the forecasting accuracy of feed-forward neural network models to the premier market modeling technique, multiplicative competitive interaction (mci) models. forecasts are compared in two separate studies: (1) the information resources inc. (iri) coffee dataset from marion, in and (2) the a. c. nielsen catsup dataset from sioux falls, sd. our results suggest neural networks are a useful substitute for mci models when there are too few observations available to estimate a fully-extended mci model. implications are discussed.
the 6th international workshop on multimedia data mining (mdm/kdd2005). in this report, we provide a summary of the issues and research directions on multimedia data mining and the outcomes of the mdm/kdd'05 workshop that was held in conjunction with the 11th acm sigkdd international conference on knowledge discovery and data mining (kdd 2005), august 21--24 in chicago il.
information diffusion through blogspace. we study the dynamics of information propagation in environments of low-overhead personal publishing, using a large collection of weblogs over time as our example domain. we characterize and model this collection at two levels. first, we present a macroscopic characterization of topic propagation through our corpus, formalizing the notion of long-running "chatter" topics consisting recursively of "spike" topics generated by outside world events, or more rarely, by resonances within the community. second, we present a microscopic characterization of propagation from individual to individual, drawing on the theory of infectious diseases to model the flow. we propose, validate, and employ an algorithm to induce the underlying propagation network from a sequence of posts, and report on the results.
learning from imbalanced data sets with boosting and data generation: the databoost-im approach. learning from imbalanced data sets, where the number of examples of one (majority) class is much higher than the others, presents an important challenge to the machine learning community. traditional machine learning algorithms may be biased towards the majority class, thus producing poor predictive accuracy over the minority class. in this paper, we describe a new approach that combines boosting, an ensemble-based learning algorithm, with data generation to improve the predictive power of classifiers against imbalanced data sets consisting of two classes. in the databoost-im method, hard examples from both the majority and minority classes are identified during execution of the boosting algorithm. subsequently, the hard examples are used to separately generate synthetic examples for the majority and minority classes. the synthetic data are then added to the original training set, and the class distribution and the total weights of the different classes in the new training set are rebalanced. the databoost-im method was evaluated, in terms of the f-measures, g-mean and overall accuracy, against seventeen highly and moderately imbalanced data sets using decision trees as base classifiers. our results are promising and show that the databoost-im method compares well in comparison with a base classifier, a standard benchmarking boosting algorithm and three advanced boosting-based algorithms for imbalanced data set. results indicate that our approach does not sacrifice one class in favor of the other, but produces high predictions against both minority and majority classes.
the download estimation task on kdd cup 2003. this paper describes our work on the download estimation task for kdd cup 2003. the task requires us to estimate how many times a paper has been downloaded in the first 60 days after it has been published on arxiv.org, a preprint server for papers on physics and related areas. the training data consists of approximately 29000 papers, the citation graph, and information about the downloads of a subset of these papers. our approach is based on an extension of the bag-of-words model, with linear svm regression as the learning algorithm. we describe our experiments with various kinds of features. we focus particularly on issues of feature construction and weighting, which turns out to be quite important for this task.
improving classification of microarray data using prototype-based feature selection. this paper addresses the problem of improving accuracy in the machine-learning task of classification from microarray data. one of the known issues specifically related to microarray data is the large number of inputs (genes) versus the small number of available samples (conditions). a promising direction of research to decrease the generalization error of classification algorithms is to perform gene selection so as to identify those genes which are potentially most relevant for the classification. classical feature selection methods are based on direct statistical methods. we present a reduction algorithm based on the notion of prototypegene. each prototype represents a set of similar gene according to a given clustering method. we present experimental evidence of the usefulness of combining prototype-based feature selection with statistical gene selection methods for the task of classifying adenocarcinoma from gene expressions.
genetic subtyping using cluster analysis. in this paper we (1) describe state-of-the-art methods to identify clusters in dna sequence data for taxonomic analysis; (2) describe a new method with better scaling properties based on model-based clustering, and (3) present examples using the nucleoprotein and hemagglutin regions of influenza and the env and gag regions of human immunodeficiency virus (hiv).
the true lift model - a novel data mining approach to response modeling in database marketing. in database marketing, data mining has been used extensively to find the optimal customer targets so as to maximize return on investment. in particular, using marketing campaign data, models are typically developed to identify characteristics of customers who are most likely to respond. while these models are helpful in identifying the likely responders, they may be targeting customers who have decided to take the desirable action or not regardless of whether they receive the campaign contact (e.g. mail, call). based on many years of business experience, we identify the appropriate business objective and its associated mathematical objective function. we point out that the current approach is not directly designed to solve the appropriate business objective. we then propose a new methodology to identify the customers whose decisions will be positively influenced by campaigns. the proposed methodology is easy to implement and can be used in conjunction with most commonly used supervised learning algorithms. an example using simulated data is used to illustrate the proposed methodology. this paper may provide the database marketing industry with a simple but significant methodological improvement and open a new area for further research and development.
statistics and data mining: intersecting disciplines. statistics and data mining have much in common, but they also have differences. the nature of the two disciplines is examined, with emphasis on their similarities and differences.
a data cleaning solution by perl scripts for the kdd cup 2003 task 2. in this paper, we present our solution for the kdd cup 2003 task 2 competition. our approach is based on a data cleaning methodology using perl scripts. these scripts contain regular expression for automatically extracting relevant information from the 35472 latex texts. these expressions were optimized by statistical investigations on the texts. our solution has permitted us to obtain 144,087 associations.
the 1st workshop on roc analysis in artificial intelligence (rocai-2004). this short report includes a summary of the presentations and discussions held during the rocai-2004 workshop, as well as the workshop conclusions and the future agenda. rocai-2004 was held in valencia, on august the 22nd, as part of the 16th european conference on artificial intelligence, ecai-2004, in valencia, spain.
resource description framework: metadata and its applications. universality, the property of the web that makes it the largest data and information source in the world, is also the property behind the lack of a uniform organization scheme that would allow easy access to data and information. a semantic web, wherein different applications and web sites can exchange information and hence exploit web data and information to their full potential, requires the information about web resources to be represented in a detailed and structured manner. resource description framework (rdf), an effort in this direction supported by the world wide web consortium, provides a means for the description of metadata which is a necessity for the next generation of interoperable web applications. the success of rdf and the semantic web will depend on (1) the development of applications that prove the applicability of the concept, (2) the availability of application interfaces which enable the development of such applications, and (3) databases and inference systems that exploit rdf to identify and locate most relevant web resources. in addition, many practical issues, such as security, ease of use, and compatibility, will be crucial in the success of rdf. this survey aims at providing a glimpse at the past, present, and future of this upcoming technology and highlights why we believe that the next generation of the web will be more organized, informative, searchable, accessible, and, most importantly, useful. it is expected that knowledge discovery and data mining can benefit from rdf and the semantic web.
artificial neural networks - a science in trouble . this article points out some very serious misconceptions about the brain in connectionism and artificial neural networks. some of the connectionist ideas have been shown to have logical flaws, while others are inconsistent with some commonly observed human learning processes and behavior. for example, the connectionist ideas have absolutely no provision for learning from stored information, something that humans do all the time. the article also argues that there is definitely a need for some new ideas about the internal mechanisms of the brain. it points out that a very convincing argument can be made for a "control theoretic" approach to understanding the brain. a "control theoretic" approach is actually used in all connectionist and neural network algorithms and it can also be justified from recent neurobiological evidence. a control theoretic approach proposes that there are subsystems within the brain that control other subsystems. hence a similar approach can be taken in constructing learning algorithms and other intelligent systems.
model builder for predictive analytics & fair isaac's approach to kdd cup 2003. fair isaac tackled the third task of kdd cup 2003 using a predictive modeling approach that leveraged citation graphs, text mining, custom variable creation and linear regression. the core tools we used were embedded in our model builder for predictive analytics (mbpa) product that makes commercially available a broad set of previously proprietary methodologies used by fair isaac for predictive scoring systems such as credit risk and credit card fraud. this short paper reviews the kdd cup problem our approach, and the toolset. we analyze the predictive variables in the model, the main sources of prediction errors, and the steps that could be taken to alleviate such errors in future work.