OpenRaiser/Intern-Atlas huggingface dataset:
Experimental design for solicitation campaigns
Authors: Uwe F. Mayer, Armand Sarkissian
Venue: KDD
Year: 2003

Data mining techniques are routinely used by fundraisers to select those prospects from a large pool of candidates who are most likely to make a financial contribution. These techniques often rely on statistical models based on trial performance data. This trial performance data is typically obtained by soliciting a smaller sample of the possible prospect pool. Collecting this trial data involves a cost; therefore the fundraiser is interested in keeping the trial size small while still collecting enough data to build a reliable statistical model that will be used to evaluate the remainder of the prospects.We describe an experimental design approach to optimally choose the trial prospects from an existing large pool of prospects. Prospects are clustered to render the problem practically tractable. We modify the standard D-optimality algorithm to prevent repeated selection of the same prospect cluster, since each prospect can only be solicited at most once.We assess the benefits of this approach on the KDD-98 data set by comparing the performance of the model based on the optimal trial data set with that of a model based on a randomly selected trial data set of equal size.
Distributed cooperative mining for information consortia
Authors: Satoshi Morinaga, Kenji Yamanishi, Jun&apos, ichi Takeuchi
Venue: KDD
Year: 2003

We consider the situation where a number of agents are distributed and each of them collects a data sequence generated according to an unknown probability distribution. Here each of the distributions is specified by common parameters and individual parameters e.g., a normal distribution with an identical mean and a different variance. Here we introduce a notion of an information consortium, which is a framework where the agents cannot show raw data to one another, but they like to enjoy significant information gain for estimating the respective distributions. Such an information consortium has recently received much interest in a broad range of areas including financial risk management, ubiquitous network mining, etc. In this paper we are concerned with the following three issues: 1) how to design a collaborative strategy for agents to estimate the respective distributions in the information consortium, 2) characterizing when each agent has a benefit in terms of information gain for estimating its distribution or information loss for predicting future data, and 3) charracterizing how much benefit each agent obtains. In this paper we yield a statistical formulation of information consortia and solve all of the above three problems for a general form of probability distributions. Specifically we propose a basic strategy for cooperative estimation and derive a necessary and sufficient condition for each agent to have a significant benefit.
Towards NIC-based intrusion detection
Authors: Matthew Eric Otey, Srinivasan Parthasarathy, Amol Ghoting, G. Li, Sundeep Narravula, Dhabaleswar K. Panda
Venue: KDD
Year: 2003

We present and evaluate a NIC-based network intrusion detection system. Intrusion detection at the NIC makes the system potentially tamper-proof and is naturally extensible to work in a distributed setting. Simple anomaly detection and signature detection based models have been implemented on the NIC firmware, which has its own processor and memory. We empirically evaluate such systems from the perspective of quality and performance (bandwidth of acceptable messages) under varying conditions of host load. The preliminary results we obtain are very encouraging and lead us to believe that such NIC-based security schemes could very well be a crucial part of next generation network security systems.
Visualizing changes in the structure of data for exploratory feature selection
Authors: Elias Pampalk, Werner Goebl, Gerhard Widmer
Venue: KDD
Year: 2003

Using visualization techniques to explore and understand high-dimensional data is an efficient way to combine human intelligence with the immense brute force computation power available nowadays. Several visualization techniques have been developed to study the cluster structure of data, i.e., the existence of distinctive groups in the data and how these clusters are related to each other. However, only few of these techniques lend themselves to studying how this structure changes if the features describing the data are changed. Understanding this relationship between the features and the cluster structure means understanding the features themselves and is thus a useful tool in the feature extraction phase.In this paper we present a novel approach to visualizing how modification of the features with respect to weighting or normalization changes the cluster structure. We demonstrate the application of our approach in two music related data mining projects.
Data-driven validation, completion and construction of event relationship networks
Authors: Chang-Shing Perng, David Thoenen, Genady Grabarnik, Sheng Ma, Joseph L. Hellerstein
Venue: KDD
Year: 2003

Event management is a focal point in building and maintaining high quality information infrastructures. We have witnessed the shift of the paradigm of event management in practice from root cause analysis (RCA) to action-oriented analysis (AOA). IBM has developed a pioneer event management methodology (EMD) based on the AOA paradigm and applied it to more than two hundred production sites with success. Foreseeably, more and more event management professionals will apply AOA in different incarnations in building proactive management facilities. By that, building correct and effective Event Relationship Networks (ERNs) becomes the dominating activity in AOA service design process. Currently, the quality of ERNs and the cost of building them largely depend on the knowledge of domain experts. We believe that we can utilize historical event logs in shortening the ERNs design process and perfecting the quality of ERNs. In this paper, we describe in detail how to apply this data-driven approach in ERN validation, completion and construction.
New unsupervised clustering algorithm for large datasets
Authors: William Peter, John Chiochetti, Clare Giardina
Venue: KDD
Year: 2003

A fast and accurate unsupervised clustering algorithm has been developed for clustering very large datasets. Though designed for very large volumes of geospatial data, the algorithm is general enough to be used in a wide variety of domain applications. The number of computations the algorithm requires is ~ O(N), and thus faster than hierarchical algorithms. Unlike the popular K-means heuristic, this algorithm does not require a series of iterations to converge to a solution. In addition, this method does not depend on initialization of a given number of cluster representatives, and so is insensitive to initial conditions. Being unsupervised, the algorithm can also "rank" each cluster based on density. The method relies on weighting a dataset to grid points on a mesh, and using a small number of rule-based agents to find the high density clusters. This method effectively reduces large datasets to the size of the grid, which is usually many orders of magnitude smaller. Numerical experiments are shown that demonstrate the advantages of this algorithm over other techniques.
Capturing best practice for microarray gene expression data analysis
Authors: Gregory Piatetsky-Shapiro, Tom Khabaza, Sridhar Ramaswamy
Venue: KDD
Year: 2003

Analyzing gene expression data from microarray devices has many important application in medicine and biology, but presents significant challenges to data mining. Microarray data typically has many attributes (genes) and few examples (samples), making the process of correctly analyzing such data difficult to formulate and prone to common mistakes. For this reason it is unusually important to capture and record good practices for this form of data mining. This paper presents a process for analyzing microarray data, including pre-processing, gene selection, randomization testing, classification and clustering; this process is captured with "Clementine Application Templates". The paper describes the process in detail and includes three case studies, showing how the process is applied to 2-class classification, multi-class classification and clustering analyses for publicly available microarray datasets.
Visualizing concept drift
Authors: Kevin B. Pratt, Gleb Tschapek
Venue: KDD
Year: 2003

We describe a visualization technique that uses brushed, parallel histograms to aid in understanding concept drift in multidimensional problem spaces. This technique illustrates the relationship between changes in distributions of multiple antecedent feature values and the outcome distribution. We can also observe effects on the relative utilization of predictive rules. Our parallel histogram technique solves the over-plotting difficulty of parallel coordinate graphs and the difficulty of comparing distributions of brushed and original data. We demonstrate our technique's usefulness in understanding concept drifts in power demand and stock investment returns.
Clinical and financial outcomes analysis with existing hospital patient records
Authors: R. Bharat Rao, Sathyakama Sandilya, Radu Stefan Niculescu, Colin Germond, Harsha Rao
Venue: KDD
Year: 2003

Existing patient records are a valuable resource for automated outcomes analysis and knowledge discovery. However, key clinical data in these records is typically recorded in unstructured form as free text and images, and most structured clinical information is poorly organized. Time-consuming interpretation and analysis is required to convert these records into structured clinical data. Thus, only a tiny fraction of this resource is utilized. We present REMIND, a Bayesian Framework for Reliable Extraction and Meaningful Inference from Nonstructured Data. REMIND integrates and blends the structured and unstructured clinical data in patient records to automatically created high-quality structured clinical data. This structuring allows existing patient records to be mined for quality assurance, regulatory compliance, and to relate financial and clinical factors. We demonstrate REMIND on two medical applications: (a) Extract "recurrence", the key outcome for measuring treatment effectiveness, for colon cancer patients (ii) Extract key diagnoses and complications for acute myocardial infarction (heart attack) patients, and demonstrate the impact of these clinical factors on financial outcomes.
Improving spatial locality of programs via data mining
Authors: Karlton Sequeira, Mohammed Javeed Zaki, Boleslaw K. Szymanski, Christopher D. Carothers
Venue: KDD
Year: 2003

In most computer systems, page fault rate is currently minimized by generic page replacement algorithms which try to model the temporal locality inherent in programs. In this paper, we propose two algorithms, one greedy and the other stochastic, designed for program specific code restructuring as a means of increasing spatial locality within a program. Both algorithms e#ectively decrease average working set size and hence the page fault rate. Our methods are more e#ective than traditional approaches due to use of domain information. We illustrate the e#cacy of our algorithms on actual data mining algorithms.
Experimental study of discovering essential information from customer inquiry
Authors: Keiko Shimazu, Atsuhito Momma, Koichi Furukawa
Venue: KDD
Year: 2003

This paper reports the result of our experimental study on a new method of applying an association rule miner to discover useful information from customer inquiry database in a call center of a company. It has been claimed that association rule mining is not suited for text mining. To overcome this problem, we propose (1) to generate sequential data set of words with dependency structure from the Japanese text database, and (2) to employ a new method for extracting meaningful association rules by applying a new rule selection criterion. Each inquiry in the sequential data was represented as a list of word pairs, each of which consists of a verb and its dependent noun. The association rules were induced regarding each pair of words as an item. The rule selection criterion comes from our principle that we put heavier weights to co-occurrence of multiple items more than single item occurrence. We regarded a rule important if the existence of the items in the rule body significantly affects the occurrence of the item in the rule head. The selected rules were then categorized to form meaningful information classes. With this method, we succeeded in extracting useful information classes from the text database, which were not acquired by only simple keyword retrieval. Also, inquiries with multiple aspects were properly classified into corresponding multiple categories.
Assessment and pruning of hierarchical model based clustering
Authors: Jeremy Tantrum, Alejandro Murua, Werner Stuetzle
Venue: KDD
Year: 2003

The goal of clustering is to identify distinct groups in a dataset. The basic idea of model-based clustering is to approximate the data density by a mixture model, typically a mixture of Gaussians, and to estimate the parameters of the component densities, the mixing fractions, and the number of components from the data. The number of distinct groups in the data is then taken to be the number of mixture components, and the observations are partitioned into clusters (estimates of the groups) using Bayes ’ rule. If the groups are well separated and look Gaussian, then the resulting clusters will indeed tend to be “distinct ” in the most common sense of the word- contiguous, densely populated areas of feature space, separated by contiguous, relatively empty regions. If the groups are not Gaussian, however, this correspondence may break down; an isolated group with a non-elliptical distribution, for example, may be modeled by not one, but several mixture components, and the corresponding clusters will no longer be well separated. We present methods for assessing the degree of separation between the components of a mixture model and between the corresponding clusters. We also propose an algorithm for pruning the cluster tree generated by hierarchical model-based clustering. The algorithm starts with the tree corresponding to the mixture model chosen by the Bayesian Information Criterion. It then progressively merges clusters that do not appear to correspond to different modes of the data density.
PaintingClass: interactive construction, visualization and exploration of decision trees
Authors: Soon Tee Teoh, Kwan-Liu Ma
Venue: KDD
Year: 2003

Decision trees are commonly used for classification. We propose to use decision trees not just for classification but also for the wider purpose of knowledge discovery, because visualizing the decision tree can reveal much valuable information in the data. We introduce PaintingClass, a system for interactive construction, visualization and exploration of decision trees. PaintingClass provides an intuitive layout and convenient navigation of the decision tree. PaintingClass also provides the user the means to interactively construct the decision tree. Each node in the decision tree is displayed as a visual projection of the data. Through actual examples and comparison with other classification methods, we show that the user can effectively use PaintingClass to construct a decision tree and explore the decision tree to gain additional knowledge.
Knowledge-based data mining
Authors: Sholom M. Weiss, Stephen J. Buckley, Shubir Kapoor, Søren Damgaard
Venue: KDD
Year: 2003

We describe techniques for combining two types of knowledge systems: expert and machine learning. Both the expert system and the learning system represent information by logical decision rules or trees. Unlike the classical views of knowledge-base evaluation or refinement, our view accepts the contents of the knowledge base as completely correct. The knowledge base and the results of its stored cases will provide direction for the discovery of new relationships in the form of newly induced decision rules. An expert system called SEAS was built to discover sales leads for computer products and solutions. The system interviews executives by asking questions, and based on the responses, recommends products that may improve a business' operations. Leveraging this expert system, we record the results of the interviews and the program's recommendations. The very same data stored by the expert system is used to find new predictive rules. Among the potential advantages of this approach are (a) the capability to spot new sales trends and (b) the substitution of less expensive probabilistic rules that use database data instead of interviews.
Screening and interpreting multi-item associations based on log-linear modeling
Authors: Xintao Wu, Daniel Barbará, Yong Ye
Venue: KDD
Year: 2003

Association rules have received a lot of attention in the data mining community since their introduction. The classical approach to find rules whose items enjoy high support (appear in a lot of the transactions in the data set) is, however, filled with shortcomings. It has been shown that support can be misleading as an indicator of how interesting the rule is. Alternative measures, such as lift, have been proposed. More recently, a paper by DuMouchel et al. proposed the use of all-two-factor loglinear models to discover sets of items that cannot be explained by pairwise associations between the items involved. This approach, however, has its limitations, since it stops short of considering higher order interactions (other than pairwise) among the items. In this paper, we propose a method that examines the parameters of the fitted loglinear models to find all the significant association patterns among the items. Since fitting loglinear models for large data sets can be computationally prohibitive, we apply graph-theoretical results to divide the original set of items into components (sets of items) that are statistically independent from each other. We then apply loglinear modeling to each of the components and find the interesting associations among items in them. The technique is experimentally evaluated with a real data set (insurance data) and a series of synthetic data sets. The results show that the technique is effective in finding interesting associations among the items involved.
The anatomy of a multimodal information filter
Authors: Yi-Leh Wu, Kingshy Goh, Beitao Li, Huaxin You, Edward Y. Chang
Venue: KDD
Year: 2003

The proliferation of objectionable information on the Internet has reached a level of serious concern. To empower end-users with the choice of blocking undesirable and offensive websites, we propose a multimodal information filter, named MORF. In this paper, we present MORF's core components: its confidence-based classifier, a Cross-bagging ensemble scheme, and multimodal classification algorithm. Empirical studies and initial statistics collected from the MORF filters deployed at sites in the U.S. and Asia show that MORF is both efficient and effective, due to our classification methods.
Distributed multivariate regression based on influential observations
Authors: Hang Yu, Ee-Chien Chang
Venue: KDD
Year: 2003

Large-scale data sets are sometimes logically and physically distributed in separate databases. The issues of mining these data sets are not just their sizes, but also the distributed nature. The complication is that communicating all the data to a central database would be too slow. To reduce communication costs, one could compress the data during transmission. Another method is random sampling. We propose an approach for distributed multivariate regression based on sampling and discuss its relationship with the compression method. The central idea is motivated by the observation that, although communication is limited, each individual site can still scan and process all the data it holds. Thus it is possible for the site to communicate only influential samples without seeing data in other sites. We exploit this observation and derive a method that provides tradeoff between communication cost and accuracy. Experimental results show that it is better than the compression method and random sampling.
XRules: an effective structural classifier for XML data
Authors: Mohammed Javeed Zaki, Charu C. Aggarwal
Venue: KDD
Year: 2003

XML documents have recently become ubiquitous because of their varied applicability in a number of applications. Classification is an important problem in the data mining domain, but current classification methods for XML documents use IR-based methods in which each document is treated as a bag of words. Such techniques ignore a significant amount of information hidden inside the documents. In this paper we discuss the problem of rule based classification of XML data by using frequent discriminatory substructures within XML documents. Such a technique is more capable of finding the classification characteristics of documents. In addition, the technique can also be extended to cost sensitive classification. We show the effectiveness of the method with respect to other classifiers. We note that the methodology discussed in this paper is applicable to any kind of semi-structured data.
Fast vertical mining using diffsets
Authors: Mohammed Javeed Zaki, Karam Gouda
Venue: KDD
Year: 2003

A number of vertical mining algorithms have been proposed recently for association mining, which have shown to be very effective and usually outperform horizontal approaches. The main advantage of the vertical format is support for fast frequency counting via intersection operations on transaction ids (tids) and automatic pruning of irrelevant data. The main problem with these approaches is when intermediate results of vertical tid lists become too large for memory, thus affecting the algorithm scalability. In this paper we present a novel vertical data representation called Diffset, that only keeps track of differences in the tids of a candidate pattern from its generating frequent patterns. We show that diffsets drastically cut down the size of memory required to store intermediate results. We show how diffsets, when incorporated into previous vertical mining methods, increase the performance significantly. We also present a new algorithm, using diffsets, for mining maximal patterns. Experimental comparisons, on both dense and sparse databases, show that diffsets deliver order of magnitude performance improvements over the best previous methods. 1
Applying data mining in investigating money laundering crimes
Authors: Zhongfei (Mark) Zhang, John J. Salerno, Philip S. Yu
Venue: KDD
Year: 2003

In this paper, we study the problem of applying data mining to facilitate the investigation of money laundering crimes (MLCs). We have identified a new paradigm of problems --- that of automatic community generation based on uni-party data, the data in which there is no direct or explicit link information available. Consequently, we have proposed a new methodology for Link Discovery based on Correlation Analysis (LDCA). We have used MLC group model generation as an exemplary application of this problem paradigm, and have focused on this application to develop a specific method of automatic MLC group model generation based on timeline analysis using the LDCA methodology, called CORAL. A prototype of CORAL method has been implemented, and preliminary testing and evaluations based on a real MLC case data are reported. The contributions of this work are: (1) identification of the uni-party data community generation problem paradigm, (2) proposal of a new methodology LDCA to solve for problems in this paradigm, (3) formulation of the MLC group model generation problem as an example of this paradigm, (4) application of the LDCA methodology in developing a specific solution (CORAL) to the MLC group model generation problem, and (5) development, evaluation, and testing of the CORAL prototype in a real MLC case data.