waashk/dblp huggingface dataset:
subspace clustering for high dimensional data: a review. subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a dataset. often in high dimensional data, many dimensions are irrelevant and can mask existing clusters in noisy data. feature selection removes irrelevant and redundant dimensions by analyzing the entire dataset. subspace clustering algorithms localize the search for relevant dimensions allowing them to find clusters that exist in multiple, possibly overlapping subspaces. there are two major branches of subspace clustering based on their search strategy. top-down algorithms find an initial clustering in the full set of dimensions and evaluate the subspaces of each cluster, iteratively improving the results. bottom-up approaches find dense regions in low dimensional spaces and combine them to form clusters. this paper presents a survey of the various subspace clustering algorithms along with a hierarchy organizing the algorithms by their defining characteristics. we then compare the two main approaches to subspace clustering using empirical scalability and accuracy tests and discuss some potential applications where subspace clustering could be particularly useful.
biokdd 2005 workshop report. bioinformatics is the science of managing, mining, and interpreting information from biological entities. genome sequencing projects have contributed to an exponential growth in complete and partial sequence databases. the structural genomics initiative aims to catalog the structure-function information for proteins. advances in technology such as microarrays have launched the subfield of genomics and proteomics to study the genes, proteins, and the regulatory gene expression circuitry inside the cell. what characterizes the state of the field is the flood of data that exists today or that is anticipated in the future; data that needs to be mined to help unlock the secrets of the cell. knowledge extracted from such analysis can be used effectively to better design new drugs, offer better medical care via diagnostic tests that combine information from multiple sources, and improve scientific and clinical practice.
data mining standards, services and platforms 2005 workshop report. this report is a summary of the workshop on data mining standards, services and platform, which was held at the kdd 2005 in chicago on august 21, 2005. the workshop included several presentations on the application of data mining standards in various systems and platforms, as well as presentations on recent changes to the pmml standard, an opening panel discussion and a concluding round-table forum.
constrained frequent pattern mining: a pattern-growth view. it has been well recognized that frequent pattern mining plays an essential role in many important data mining tasks. however, frequent pattern mining often generates a very large number of patterns and rules, which reduces not only the efficiency but also the effectiveness of mining. recent work has highlighted the importance of the constraint-based mining paradigm in the context of mining frequent itemsets, associations, correlations, sequential patterns, and many other interesting patterns in large databases.recently, we developed efficient pattern-growth methods for frequent pattern mining. interestingly, pattern-growth methods are not only efficient but also effective in mining with various constraints. many tough constraints which cannot be handled by previous methods can be pushed deep into the pattern-growth mining process. in this paper, we overview the principles of pattern-growth methods for constrained frequent pattern mining and sequential pattern mining. moreover, we explore the power of pattern-growth methods towards mining with tough constraints and highlight some interesting open problems.
mining logs files for data-driven system management. with advancement in science and technology, computing systems are becoming increasingly more complex with an increasing variety of heterogeneous software and hardware components. they are thus becoming increasingly more difficult to monitor, manage and maintain. traditional approaches to system management have been largely based on domain experts through a knowledge acquisition process that translates domain knowledge into operating rules and policies. this has been well known and experienced as a cumber-some, labor intensive, and error prone process. in addition, this process is difficult to keep up with the rapidly changing environments. there is thus a pressing need for automatic and efficient approaches to monitor and manage complex computing systems.a popular approach to system management is based on analyzing system log files. however, some new aspects of the log files have been less emphasized in existing methods from data mining and machine learning community. the various formats and relatively short text messages of log files, and temporal characteristics in data representation pose new challenges. in this paper, we will describe our research efforts on mining system log files for automatic management. in particular, we apply text mining techniques to categorize messages in log files into common situations, improve categorization accuracy by considering the temporal characteristics of log messages, and utilize visualization tools to evaluate and validate the interesting temporal patterns for system management.
p-tree classification of yeast gene deletion data. genomics data has many properties that make it different from "typical" relational data. the presence of multi-valued attributes as well as the large number of null values led us to a p-tree-based bit-vector representation in which matching 1-values were counted to evaluate similarity between genes. quantitative information such as the number of interactions was also included in the classifier. interaction information allowed us to extend the known properties of one protein with information on its interacting neighbors. different feature attributes were weighted independently. relevance of different attributes was systematically evaluated through optimization of weights using a genetic algorithm. the aroc value for the classified list was used as the fitness function for the genetic algorithm.
discovery in multi-attribute data with user-defined constraints. there has been a growing interest in mining frequent itemsets in relational data with multiple attributes. a key step in this approach is to select a set of attributes that group data into transactions and a separate set of attributes that labels data into items. unsupervised and unrestricted mining, however, is stymied by the combinatorial complexity and the quantity of patterns as the number of attributes grows. in this paper, we focus on leveraging the semantics of the underlying data for mining frequent itemsets. for instance, there are usually taxonomies in the data schema and functional dependencies among the attributes. domain knowledge and user preferences often have the potential to significantly reduce the exponentially growing mining space. these observations motivate the design of a user-directed data mining framework that allows such domain knowledge to guide the mining process and control the mining strategy. we show examples of tremendous reduction in computation by using domain knowledge in mining relational data with multiple attributes.
the 4th international workshop on multimedia data mining (mdm/kdd2003). in this short report we provide a summary of the presentations, conclusions and directions of future work that were discussed during mdm/kdd2003 workshop, held in conjunction with the 9th acm sigkdd international conference on knowledge discovery and data mining, august 27, 2003 at washington, dc, usa.
winning the kdd99 classification cup: bagged boosting. we briefly describe our approach for the kdd99 classification cup. the solution is essentially a mixture of bagging and boosting. additionally, asymmetric error costs are taken into account by minimizing the so-called conditional risk. furthermore, the standard sampling with replacement methodology of bagging was modified to put a specific focus on the smaller but expensive-if-predicted-wrongly classes.
the weka solution to the 2004 kdd cup. this short communication describes the weka solution for the 2004 kdd cup problems, mostly focusing on the bioinformatics problem, where this approach performed best among all submissions. differences were not significant for the best three submissions, though. the identical setup trained for the physics problem achieved rank nineteen, which is still reasonable.
minority report in fraud detection: classification of skewed data. this paper proposes an innovative fraud detection method, built upon existing fraud detection research and minority report, to deal with the data mining problem of skewed data distributions. this method uses backpropagation (bp), together with naive bayesian (nb) and c4.5 algorithms, on data partitions derived from minority oversampling with replacement. its originality lies in the use of a single meta-classifier (stacking) to choose the best base classifiers, and then combine these base classifiers' predictions (bagging) to improve cost savings (stacking-bagging). results from a publicly available automobile insurance fraud detection data set demonstrate that stacking-bagging performs slightly better than the best performing bagged algorithm, c4.5, and its best classifier, c4.5 (2), in terms of cost savings. stacking-bagging also outperforms the common technique used in industry (bp without both sampling and partitioning). subsequently, this paper compares the new fraud detection method (meta-learning approach) against c4.5 trained using undersampling, oversampling, and smoteing without partitioning (sampling approach). results show that, given a fixed decision threshold and cost matrix, the partitioning and multiple algorithms approach achieves marginally higher cost savings than varying the entire training data set with different class distributions. the most interesting find is confirming that the combination of classifiers to produce the best cost savings has its contributions from all three algorithms.
kdnuggets interview with usama fayyad. kdnuggets newsletter has a new section of interviews with leaders in the field. this article presents the interview with usama fayyad, president and ceo of digimine.
kdnuggets interview with jesus mena. the kdnuggets newsletter has a new section of interviews with leaders in the field. this article presents the interview with jesus mena, ceo of webminer.
cryptographic techniques for privacy-preserving data mining. research in secure distributed computation, which was done as part of a larger body of research in the theory of cryptography, has achieved remarkable results. it was shown that non-trusting parties can jointly compute functions of their different inputs while ensuring that no party learns anything but the defined output of the function. these results were shown using generic constructions that can be applied to any function that has an efficient representation as a circuit. we describe these results, discuss their efficiency, and demonstrate their relevance to privacy preserving computation of data mining algorithms. we also show examples of secure computation of data mining algorithms that use these generic constructions.
using text mining and natural language processing for health care claims processing. a health care claims processing application is introduced which processes both structured and unstructured information associated with medical insurance claims. the application makes use of a natural language processing (nlp) engine, together with application-specific knowledge, written in a concept specification language. using nlp techniques, the entities and relationships that act as indicators of recoverable claims are mined from management notes, call centre logs and patient records to identify medical claims that require further investigation. text mining techniques can then be applied to find dependencies between different entities, and to combine indicators to provide scores to individual claims. claims are scored to determine whether they involve potential fraud or abuse, or to determine whether claims should be paid by or in conjunction with other insurers or organizations. dependencies between claims and other records can then be combined to create cases. issues related to the design of the application are discussed, specifically the use of rule-based techniques which provide a capability for deeper analysis than traditionally found in statistical techniques.
classification trees for problems with monotonicity constraints. for classification problems with ordinal attributes very often the class attribute should increase with each or some of the explaining attributes. these are called classification problems with monotonicity constraints. classical decision tree algorithms such as cart or c4.5 generally do not produce monotone trees, even if the dataset is completely monotone. this paper surveys the methods that have so far been proposed for generating decision trees that satisfy monotonicity constraints. a distinction is made between methods that work only for monotone datasets and methods that work for monotone and non-monotone datasets alike.
a perspective on inductive databases. inductive databases tightly integrate databases with data mining. the key ideas are that data and patterns (or models) are handled in the same way and that an inductive query language allows the user to query and manipulate the patterns (or models) of interest.this paper proposes a simple and abstract model for inductive databases. we describe the basic formalism, a simple but fairly powerful inductive query language, some basics of reasoning for query optimization, and discuss some memory organization and implementation issues.
probabilistic logic learning. the past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the intersection of probabilistic reasoning, logical representations, and machine learning. a rich variety of different formalisms and learning techniques have been developed. this paper provides an introductory survey and overview of the state-of-the-art in probabilistic logic learning through the identification of a number of important probabilistic, logical and learning concepts.
discovering informative connection subgraphs in multi-relational graphs. discovering patterns in graphs has long been an area of interest. in most approaches to such pattern discovery either quantitative anomalies, frequency of substructure or maximum flow is used to measure the interestingness of a pattern. in this paper we introduce heuristics that guide a subgraph discovery algorithm away from banal paths towards more "informative" ones. given an rdf graph a user might pose a question of the form: "what are the most relevant ways in which entity x is related to entity y?" the response to which is a subgraph connecting x to y. we use our heuristics to discover informative subgraphs within rdf graphs. our heuristics are based on weighting mechanisms derived from edge semantics suggested by the rdf schema. we present an analysis of the quality of the subgraphs generated with respect to path ranking metrics. we then conclude presenting intuitions about which of our weighting schemes and heuristics produce higher quality subgraphs.
extreme re-balancing for svms: a case study. there are many practical applications where learning from single class examples is either, the only possible solution, or has a distinct performance advantage. the first case occurs when obtaining examples of a second class is difficult, e.g., classifying sites of "interest" based on web accesses. the second situation is exemplified by the gene knock-out experiments for understanding aryl hydrocarbon receptor signalling pathway that provided the data for the second task of the kdd 2002 cup, where minority one-class svms significantly outperform models learnt using examples from both classes.this paper explores the limits of supervised learning of a two class discrimination from data with heavily unbalanced class proportions. we focus on the case of supervised learning with support vector machines. we consider the impact of both sampling and weighting imbalance compensation techniques and then extend the balancing to extreme situations when one of the classes is ignored completely and the learning is accomplished using examples from a single class.our investigation with the data for kdd 2002 cup as well as text benchmarks such as reuters newswire shows that there is a consistent pattern of performance differences between one and two-class learning for all svms investigated, and these patterns persist even with aggressive dimensionality reduction through automated feature selection. using insight gained from the above analysis, we generate synthetic data showing similar pattern of performance.
the case for anomalous link discovery. in this paper, we describe the challenges inherent to the link prediction (lp) problem in multirelational data mining, and explore the reasons why many lp models have performed poorly. we present the alternate (and complimentary) task of anomalous link discovery (ald) and qualitatively demonstrate the effectiveness of simple lp models for the ald task.
exploratory medical knowledge discovery: experiences and issues. the application of data mining and knowledge discovery techniques to medical and health datasets is a rewarding but highly challenging area. not only are the datasets large, complex, heterogeneous, hierarchical, time-varying and of varying quality but there exists asubstantial medical knowledge base which demands a robust collaboration between the data miner and the health professional(s) if useful information is to be extracted.this paper presents the experiences of the authors and others in applying exploratory data mining techniques to medical, health and clinical data. in so doing, it elicits a number of general issues and provides pointers to possible areas of future research in data mining and knowledge discovery more broadly.
what's interesting about cricket? - on thresholds and anticipation in discovered rules. despite significant progress, determining the interestingness of a rule remains a difficult problem. this short paper investigates the lessons that may be learned from analysing the (largely manual) selection of interesting statistics for cricket (or any other data rich sport) by experts. in particular, the effect of thresholds on the interestingness of rules describing events in the sporting arena is discussed. the concept of anticipation is shown also to be critical in this selection and to vary the level of interest in events that may contribute to the achievement of a threshold value during a match, thus adding a temporal dimension to interestingness. this temporal aspect can be best modelled on the single-past-branching-future model of time. as a result of this investigation, a few new general ideas are discussed that add to the research in this area. significantly, some of the new criteria are implicitly temporal in that they rely on a model of behaviour over time. the applicability of threshold values for detecting uncharacteristically poor performances are canvassed as areas of interest yet to be explored.
a bibliography of temporal, spatial and spatio-temporal data mining research. with the growth in the size of datasets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. at the same time, a greater recognition of the value of temporal and spatial data has been evident and the first papers looking at the confluence of these two areas are starting to emerge. this short paper provides a few comments on this research and provides a bibliography of relevant research papers investigating temporal, spatial and spatio-temporal data mining.
extracting relational data from html repositories. there is a vast amount of valuable information in html documents, widely distributed across the world wide web and across corporate intranets. unfortunately, html is mainly presentation oriented and hard to query. in this paper, we develop a system to extract desired information (records) from thousands of html documents, starting from a small set of examples. duplicates in the result are automatically detected and eliminated. we propose a novel method to estimate the current coverage of results by the system, based on capture-recapture models with unequal capture probabilities. we also propose techniques for estimating the error rate of the extracted information and an interactive the technique for enhancing information quality. to evaluate the method and ideas proposed in this paper, we conducted an extensive set of experiments. our experimental results validate the effectiveness and utility of our system, and demonstrate interesting tradeoffs between running time of information extraction and coverage of results.
machine learning in low-level microarray analysis. machine learning and data mining have found a multitude of successful applications in microarray analysis, with gene clustering and classification of tissue samples being widely cited examples. low-level microarray analysis -- often associated with the pre-processing stage within the microarray life-cycle -- has increasingly become an area of active research, traditionally involving techniques from classical statistics. this paper explores opportunities for the application of machine learning and data mining methods to several important low-level microarray analysis problems: monitoring gene expression, transcript discovery, genotyping and resequencing. relevant methods and ideas from the machine learning community include semi-supervised learning, learning from heterogeneous data, and incremental learning.
data-driven modeling and prediction of acute toxicity of pesticide residues. this paper outlines and implements a concept for developing alternative tools for toxicity modeling and prediction of chemical compounds to be used for evaluation and authorization purposes of public regulatory bodies to help minimizing animal tests, costs, and time associated with registration and risk assessment processes. starting from a general problem description we address and introduce concepts of multileveled self-organization for high-dimensional modeling, model validation, model combining, and decision support within the frame of a knowledge discovery from noisy data.
learning to extract information from large domain-specific websites using sequential models. in this article we describe a novel information extraction task on the web and show how it can be solved effectively using the emerging conditional exponential models. the task involves learning to find specific goal pages on large domain-specific websites. an example of such a task is to find computer science publications starting from university root pages. we encode this as a sequential labeling problem solved using conditional random fields (crfs). these models enable us to exploit a wide variety of features including keywords and patterns extracted from and around hyperlinks and html pages, dependency among labels of adjacent pages, and existing databases of named entities in a unified probabilistic framework. this is an important advantage over previous rule-based or generative models for tackling the challenges of diversity on web data.