waashk/dblp huggingface dataset:
the myth of the double-blind review?: author identification using only citations. prior studies have questioned the degree of anonymity of the double-blind review process for scholarly research articles. for example, one study based on a survey of reviewers concluded that authors often could be identified by reviewers using a combination of the author's reference list and the referee's personal background knowledge. for the kdd cup 2003 competition's "open task," we examined how well various automatic matching techniques could identify authors within the competition's very large archive of research papers. this paper describes the issues surrounding author identification, how these issues motivated our study, and the results we obtained. the best method, based on discriminative self-citations, identified authors correctly 40--45% of the time. one main motivation for double-blind review is to eliminate bias in favor of well-known authors. however, identification accuracy for authors with substantial publication history is even better (60% accuracy for the top-10% most prolific authors, 85% for authors with 100 or more prior papers).
kdd-cup 2004: results and analysis. this paper summarizes and analyzes the results of the 2004 kdd-cup. the competition consisted of two tasks from the areas of particle physics and protein homology detection. it focused on the problem of optimizing supervised learning to different performance measures (accuracy, cross-entropy, roc area, slac-q, squared error, average precision, top 1, and rank of last). a total of 102 groups participated in the competition, 6 of which received awards or honorable mentions. their approaches are described in other papers in this issue of sigkdd explorations. in this paper we do not analyze any particular approach, but give insight into the performance of the field of competitors as a whole. we study what fraction of the participants found good solutions, how well participants were able to optimize to different performance measures, how homogeneous their submitted predictions are, and if the best submissions represent the maximal performances that could reasonably be achieved. we are keeping the kdd-cup 2004 www site open and have added an automatic scoring system for new submissions in order to encourage further research in this area.
is pushing constraints deeply into the mining algorithms really what we want?: an alternative approach for association rule mining. the common approach to exploit mining constraints is to push them deeply into the mining algorithms. in our paper we argue that this approach is based on an understanding of kdd that is no longer up-to-date. in fact, today kdd is seen as a human centered, highly interactive and iterative process. blindly enforcing constraints already during the mining runs neglects the process character of kdd and therefore is no longer state of the art. constraints can make a single algorithm run faster but in fact we are still far from response times that would allow true interactivity in kdd. in addition we pay the price of repeated mining runs and moreover risk reducing data mining to some kind of hypothesis testing. taking all the above into consideration we propose to do exactly the contrary of constrained mining: we accept an initial (nearly) unconstrained and costly mining run. but instead of a sequence of subsequent and still expensive constrained mining runs we answer all further mining queries from this initial result set. whereas this is straight forward for constraints that can be implemented as filters on the result set, things get more complicated when we restrict the underlying mining data. actually in practice such constraints are very important, e.g. the generation of rules for certain days of the week, for families, singles, male or female customers etc. we show how to postpone such row-restriction constraints on the transactions from rule generation to rule retrieval from the initial result set.
a multistrategy approach for digital text categorization from imbalanced documents. the goal of the research described here is to develop a multistrategy classifier system that can be used for document categorization. the system automatically discovers classification patterns by applying several empirical learning methods to different representations for preclassified documents belonging to an imbalanced sample. the learners work in a parallel manner, where each learner carries out its own feature selection based on evolutionary techniques and then obtains a classification model. in classifying documents, the system combines the predictions of the learners by applying evolutionary techniques as well. the system relies on a modular, flexible architecture that makes no assumptions about the design of learners or the number of learners available and guarantees the independence of the thematic domain.
data mining for hypertext: a tutorial survey. with over 800 million pages covering most areas of human endeavor, the world-wide web is a fertile ground for data mining research to make a difference to the effectiveness of information search. today, web surfers access the web through two dominant interfaces: clicking on hyperlinks and searching via keyword queries. this process is often tentative and unsatisfactory. better support is needed for expressing one's information need and dealing with a search result in more structured ways than available now. data mining and machine learning have significant roles to play towards this end.in this paper we will survey recent advances in learning and mining problems related to hypertext in general and the web in particular. we will review the continuum of supervised to semi-supervised to unsupervised learning problems, highlight the specific challenges which distinguish data mining in the hypertext domain from data mining in the context of data warehouses, and summarize the key areas of recent and ongoing research.
mining semantics for large scale integration on the web: evidences, insights, and challenges. the web has been rapidly "deepened" -- with myriad searchable databases online, where data are hidden behind query interfaces. toward large scale integration over this "deep web," we are facing a new challenge- with its dynamic and ad-hoc nature, such large scale integration mandates dynamic semantics discovery. that is, we must on-the-fly cope with "semantics" of dynamically discovered sources without pre-configured source-specific knowledge. to tackle this challenge, our initial works hinge on the insight that the large scale is itself also a unique opportunity: we observe that the desired "semantics" often connects to surface presentation characteristics, through some hidden regularities over many sources. such regularities can be essentially leveraged in enabling semantics discovery. in particular, we report our evidences in three initial tasks for integrating the deep web: interface extraction, schema matching, and query translation. generalizing these specific evidences, we thus propose our "unified insight" of "mining" semantics for large scale integration by exploiting hidden regularities across holistic sources. further, to fulfill the promise of such holistic mining, we discuss challenges toward its realization for dynamic semantics discovery. as our initial works as well as several related efforts have witnessed, we believe our unified insight, holistic mining for semantics discovery, is a promising methodology toward enabling large scale integration.
graph-based relational learning: current and future directions. graph-based relational learning (gbrl) differs from logic-based relational learning, as addressed by inductive logic programming techniques, and differs from frequent subgraph discovery, as addressed by many graph-based data mining techniques. learning from graphs, rather than logic, presents representational issues both in input data preparation and output pattern language. while a form of graph-based data mining, gbrl focuses on identifying novel, not necessarily most frequent, patterns in a graph-theoretic representation of data. this approach to graph-based data mining provides both simplifications and challenges over frequency-based approaches. in this paper we discuss these issues and future directions of graph-based relational learning.
kdd-99: the fifth acm sigkdd international conference on knowledge discovery and data mining. kdd-99 was the fifth conference in the kdd series attracting over 200 high quality submissions and almost 600 attendees. here we describe some of the highlights of the technical program.
a workshop report: mining for and from the semantic web at kdd 2004. the international workshop on mining for and from the semantic web (msw) at the kdd 2004 successfully brought together people from the communities semantic web and knowledge discovery. the goal of the workshop was to strengthen the communication and interaction between these communities as there is currently an agreement that both can benefit largely from each other. overall the contributions and discussions showed that today mining for the semantic web is the primarily targeted research area. however, with a growing amount of available semantic web data also mining from the semantic web will be more prominent.
data snooping, dredging and fishing: the dark side of data mining, a sigkdd99 panel report. this article briefly describes a panel discussion at sigkdd99.
kdd cup 2001 report. this paper presents results and lessons from kdd cup 2001. kdd cup 2001 focused on mining biological databases. it involved three cutting-edge tasks related to drug design and genomics.
towards interactive exploration of gene expression patterns. analyzing coherent gene expression patterns is an important task in bioinformatics research and biomedical applications. recently, various clustering methods have been adapted or proposed to identify clusters of co-expressed genes and recognize coherent expression patterns as the centroids of the clusters. however, the interpretation of co-expressed genes and coherent patterns mainly depends on the domain knowledge, which presents several challenges for coherent pattern mining and cannot be solved by most existing clustering approaches.in this paper, we introduce an interactive exploration system genex (gene explorer) for mining coherent expression patterns. we develop a novel coherent pattern index graph to provide highly confident indications of the existence of coherent patterns. typical exploration operations are supported based on the index graph. we also provide a bunch of graphical views as the user interface to visualize the data set and facilitate the interactive operations. to help users to interpret and validate the mining results, we design the gene annotation panel that connects the genes with some public annotation databases. the experimental results show that our approach is more effective than the state-of-the-art methods in mining real gene expression data sets.
learning by googling. the goal of giving a well-defined meaning to information is currently shared by endeavors such as the semantic web as well as by current trends within knowledge management. they all depend on the large-scale formalization of knowledge and on the availability of formal metadata about information resources. however, the question how to provide the necessary formal metadata in an effective and efficient way is still not solved to a satisfactory extent. certainly, the most effective way to provide such metadata as well as formalized knowledge is to let humans encode them directly into the system, but this is neither efficient nor feasible. furthermore, as current social studies show, individual knowledge is often less powerful than the collective knowledge of a certain community.as a potential way out of the knowledge acquisition bottleneck, we present a novel methodology that acquires collective knowledge from the world wide web using the googletm api. in particular, we present pankow, a concrete instantiation of this methodology which is evaluated in two experiments: one with the aim of classifying novel instances with regard to an existing ontology and one with the aim of learning sub-/superconcept relations.
class imbalances versus small disjuncts. it is often assumed that class imbalances are responsible for significant losses of performance in standard classifiers. the purpose of this paper is to the question whether class imbalances are truly responsible for this degradation or whether it can be explained in some other way. our experiments suggest that the problem is not directly caused by class imbalances, but rather, that class imbalances may yield small disjuncts which, in turn, will cause degradation. we argue that, in order to improve classifier performance, it may, then, be more useful to focus on the small disjuncts problem than it is to focus on the class imbalance problem. we experiment with a method that takes the small disjunct problem into consideration, and show that, indeed, it yields a performance superior to the performance obtained using standard or advanced solutions to the class imbalance problem.
learning missing values from summary constraints. real-world data sets often contain errors and inconsistency. even though this is a very important problem it has received relatively little attention in the research community. in this paper we examine the problem of learning missing values when some summary information is available. we use linear algebra and constraint programming techniques to learn the missing values using apriori-known summary information and that derived from the raw data. we reconstruct the missing values by different methods in three scenarios: ideal-constrained, under-constrained, and over-constrained. furthermore, for a range query involving missing values, we also give the lower bound and upper bound for the values using constraint programming techniques. we believe that theory of linear algebra and constraint programming constitutes a sound basis for learning missing values when summary information is available.
behind-the-scenes data mining: a report on the kdd-98 panel. successful technology becomes invisible. few people think much about internal combustion engines while they drive to work in three-thousand-pound hunks of metal powered by them, or electricity while it enables countless parts of their modern lives. data mining has a long way to go before it succeeds in this way -- or does it? at kdd-98, the behind-the-scenes data mining panel presented five views on this aspect of the success of data mining. george h. john, data mining guru at e.piphany, moderated the panel which included usama fayyad, senior researcher at microsoft; elliot fishman, director of product management at doubleclick; gerald fahner, project analyst at fair isaac & co.; and paul dubose, cto of analytika. the story below is a fictional amalgam of their presentations, seen from the viewpoint of an average citizen -- a day in the life of john q. record.
the genomics of a signaling pathway: a kdd cup challenge task. this article describes task 2 of the kdd cup 2002 datamining competition. the task involved predicting whether deletions of individual genes in a yeast genome would affect a particular signaling pathway in the cell. with its rich data sources and abundance of missing information, the task is representative of data mining problems in genomics and proved to be quite challenging.
statistical methods for joint data mining of gene expression and dna sequence database. one of the purposes of microarray gene expression experiments is to identify genes regulated under specific cellular conditions. with the availability of putative transcription factor binding motifs, it is now possible to relate gene expression pattern to the pattern of transcription factor binding sites (tfbs), as well as study how tfbs interact with each other to control gene expression. the objectives of this study are to develop a systematic approach for combining data from microarray gene expression experiments and the corresponding regulatory motif patterns in order to delineate gene regulation mechanisms. a secondary goal is to develop a predictive model for finding similarly regulated genes. three consecutive procedures are proposed for such data mining activities. first, a linear mixed-effect model is fit to data from microarray gene expression experiments and potential regulated (positive) genes are identified based on a specific biological hypothesis. putative tfbs are then retrieved for the identified positive genes and randomly selected controls. second, a cluster analysis is conducted to reduce collinearity among the binding sites. in the third step, logistic regression is applied to choose the best model to predict gene type (positive, control) based on the numerous tfbs predictors. the above approach was applied to an internal example and a model was developed to predict up-regulated genes in activated t-helper (th) cells. using a leave-one-out cross- validation scheme, the model has an 18.9% false positive rate and a 41.7% false negative rate.
report on kdd conference 2004 panel discussion can natural language processing help text mining? with large amounts of text data now available on-line, both on the internet and in corporate repositories, text mining is an area of growing interest. historically, data mining researchers have come out of the statistics, database and machine learning communities. despite a few exceptions, it has by and large had little interaction with the computational linguistics and natural language processing (nlp) community. given that the subject matter of text mining is free text, one might naturally assume techniques developed over the last few decades in computational linguistics and nlp, should make big contributions towards the younger field of text mining. however, other than in the area of information extraction, empirical evidence has not borne this out.
text mining and natural language processing: introduction for the special issue. this paper provides an introduction to this special issue of sigkdd explorations devoted to natural language processing and text mining.
when and how to subsample: report on the kdd-2001 panel. databases in the terabyte range are now common. in many domains, mining all the data available in reasonable time is already beyond the reach of current systems. yet the size of databases continues to grow rapidly. is subsampling unavoidable? or should it be avoided at all costs? if we subsample, what is the best way to do it? what issues must be taken into account? the kdd-2001 panel on when and how to subsample addressed these and related questions, with the twin goals of developing practical guidelines and identifying key research issues. it was chaired by pedro domingos (university of washington), and the participants were surajit chaudhuri (microsoft research), david jensen (university of massachusetts at amherst), ronny kohavi (blue martini), and foster provost (new york university). below is each panelist's summary of his position.
the ferrety algorithm for the kdd cup 2005 problem. in this paper, we present a general solution for the kdd cup 2005 problem. it uses the internet as source of knowledge and extends it to categorize very short (less than 5 words) documents with reasonable accuracy. our approach consists of three main parts: i.) a central knowledge filter ii.) an on-demand web crawler and iii.) a very efficient categorizer system. our solution obtained creativity and precision runner-up awards at the competition. the main idea of ferrety algorithm can be generalized for mapping one taxonomy to another if training documents are available.
prospects and challenges for multi-relational data mining. this short paper argues that multi-relational data mining has a key role to play in the growth of kdd, and briefly surveys some of the main drivers, research problems, and opportunities in this emerging field.
mining structures for semantics. online data is available in two avors: unstructured data that resides as free text in html pages, and structured data that resides in databases and knowledge bases. unstructured data is easily accessed as human-readable text on a browser, while structured data is hidden behind web query interfaces (web forms), web services, and custom database apis. access to this data, popularly referred to as the hidden web, entails submitting correctly completed web forms or writing code to access web services using protocols such as soap.
mobimine: monitoring the stock market from a pda. this paper describes an experimental mobile data mining system that allows intelligent monitoring of time-critical financial data from a hand-held pda. it presents the overall system architecture and the philosophy behind the design. it explores one particular aspect of the system---automated construction of personalized focus area that calls for user's attention. this module works using data mining techniques. the paper describes the data mining component of the system that employs a novel fourier analysis-based approach to efficiently represent, visualize, and communicate decision trees over limited bandwidth wireless networks. the paper also discusses a quadratic programming-based personalization module that runs on the pdas and the multi-media based user-interfaces. it reports experimental results using an ad hoc peer-to-peer ieee 802.11 wireless network.
loss-based estimation with cross-validation: applications to microarray data analysis. current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions, with typically unknown and intricate correlation patterns among variables. addressing these inference questions satisfactorily requires: (i) an intensive and thorough search of the parameter space to generate good candidate estimators; (ii) an approach for selecting an optimal estimator among these candidates; and (iii) a method for reliably assessing the performance of the resulting estimator. we propose a unified loss-based methodology for estimator construction, selection, and performance assessment with cross-validation. in this approach, the parameter of interest is defined as the risk minimizer for a suitable loss function and candidate estimators are generated using this (or possibly another) loss function. cross-validation is applied to select an optimal estimator among the candidates and to assess the overall performance of the resulting estimator. this general estimation framework encompasses a number of problems which have traditionally been treated separately in the statistical literature, including multivariate outcome prediction and density estimation based on either uncensored or censored data. this article provides an overview of the methodology and describes its application to the prediction of biological and clinical outcomes (possibly censored) using microarray gene expression measures.
a machine learning approach for the curation of biomedical literature - kdd cup 2002 (task 1). in this paper, we present an automated text classification system for the classification of biomedical papers. this classification is based on whether there is experimental evidence for the expression of molecular gene products for specified genes within a given paper. the system performs pre-processing and data cleaning, followed by feature extraction from the raw text. it subsequently classifies the paper using the extracted features with a na&iuml;ve bayes classifier. our approach has made it possible to classify (and curate) biomedical papers automatically, thus potentially saving considerable time and resources.