OpenRaiser/Intern-Atlas huggingface dataset:
Automated cyclone discovery and tracking using knowledge sharing in multiple heterogeneous satellite data
Authors: Shen-Shyang Ho, Ashit Talukder
Venue: KDD
Year: 2008

Current techniques for cyclone detection and tracking employ NCEP (National Centers for Environmental Prediction) models from in-situ measurements. This solution does not provide true global coverage, unlike remote satellite observations. However it is impractical to use a single Earth orbiting satellite to detect and track events such as cyclones in a continuous manner due to limited spatial and temporal coverage. One solution to alleviate such persistent problems is to utilize heterogeneous sensor data from multiple orbiting satellites. However, this solution requires overcoming other new challenges such as varying spatial and temporal resolution between satellite sensor data, the need to establish correspondence between features from different satellite sensors, and the lack of definitive indicators for cyclone events in some sensor data.
Permu-pattern: discovery of mutable permutation patterns with proximity constraint
Authors: Meng Hu, Jiong Yang, Wei Su
Venue: KDD
Year: 2008

Pattern discovery in sequences is an important problem in many applications, especially in computational biology and text mining. However, due to the noisy nature of data, the traditional sequential pattern model may fail to reflect the underlying characteristics of sequence data in these applications. There are two challenges: First, the mutation noise exists in the data, and therefore symbols may be misrepresented by other symbols; Secondly, the order of symbols in sequences could be permutated. To address the above problems, in this paper we propose a new sequential pattern model called mutable permutation patterns. Since the Apriori property does not hold for our permutation pattern model, a novel Permu-pattern algorithm is devised to mine frequent mutable permutation patterns from sequence databases. A reachability property is identified to prune the candidate set. Last but not least, we apply the permutation pattern model to a real genome dataset to discover gene clusters, which shows the effectiveness of the model. A large amount of synthetic data is also utilized to demonstrate the efficiency of the Permu-pattern algorithm.
DiMaC: a disguised missing data cleaning tool
Authors: Ming Hua, Jian Pei
Venue: KDD
Year: 2008

In some applications such as filling in a customer information form on the web, some missing values may not be explicitly represented as such, but instead appear as potentially valid data values. Such missing values are known as disguised missing data, which may impair the quality of data analysis severely. The very limited previous studies on cleaning disguised missing data highly rely on domain background knowledge in specific applications and may not work well for the cases where the disguise values are inliers.
Bridging centrality: graph mining from element level to group level
Authors: Woochang Hwang, Taehyong Kim, Murali Ramanathan, Aidong Zhang
Venue: KDD
Year: 2008

Despite the pervasiveness of networks as models for real world systems ranging from the Internet, the World Wide Web to gene regulation and scientific collaborations, only a limited number of metrics capable of characterizing these systems are available. The existing metrics for characterizing networks have broad specificity and lack the selectivity for many applications. The purpose of this paper is to identify and critically evaluate a metric, termed bridging centrality, which is highly selective for identifying bridges in networks. The properties of bridges are unique compared to the other network metrics. For a diverse range of data sets, we found that networks are highly susceptible to disruption but robust to loss structural integrity upon targeted deletion of bridging nodes. A novel graph clustering approach, termed `bridge cut', utilizing bridging edges as module boundary is also proposed. The modules identified by the bridge cut algorithm are more effective than the other graph clustering methods. Thus, bridging centrality is a network metric with unique properties that may aid in network analysis from element to group level in various areas including systems biology and national security applications.
Fast logistic regression for text categorization with variable-length n-grams
Authors: Georgiana Ifrim, Gökhan H. Bakir, Gerhard Weikum
Venue: KDD
Year: 2008

A common representation used in text categorization is the bag of words model (aka. unigram model). Learning with this particular representation involves typically some preprocessing, e.g. stopwords-removal, stemming. This results in one explicit tokenization of the corpus. In this work, we introduce a logistic regression approach where learning involves automatic tokenization. This allows us to weaken the a-priori required knowledge about the corpus and results in a tokenization with variable-length (word or character) n-grams as basic tokens. We accomplish this by solving logistic regression using gradient ascent in the space of all ngrams. We show that this can be done very efficiently using a branch and bound approach which chooses the maximum gradient ascent direction projected onto a single dimension (i.e., candidate feature). Although the space is very large, our method allows us to investigate variable-length n-gram learning. We demonstrate the efficiency of our approach compared to state-of-the-art classifiers used for text categorization such as cyclic coordinate descent logistic regression and support vector machines.
Automatic identification of quasi-experimental designs for discovering causal knowledge
Authors: David D. Jensen, Andrew S. Fast, Brian J. Taylor, Marc E. Maier
Venue: KDD
Year: 2008

Researchers in the social and behavioral sciences routinely rely on quasi-experimental designs to discover knowledge from large data-bases. Quasi-experimental designs (QEDs) exploit fortuitous circumstances in non-experimental data to identify situations (sometimes called "natural experiments") that provide the equivalent of experimental control and randomization. QEDs allow researchers in domains as diverse as sociology, medicine, and marketing to draw reliable inferences about causal dependencies from non-experimental data. Unfortunately, identifying and exploiting QEDs has remained a painstaking manual activity, requiring researchers to scour available databases and apply substantial knowledge of statistics. However, recent advances in the expressiveness of databases, and increases in their size and complexity, provide the necessary conditions to automatically identify QEDs. In this paper, we describe the first system to discover knowledge by applying quasi-experimental designs that were identified automatically. We demonstrate that QEDs can be identified in a traditional database schema and that such identification requires only a small number of extensions to that schema, knowledge about quasi-experimental design encoded in first-order logic, and a theorem-proving engine. We describe several key innovations necessary to enable this system, including methods for automatically constructing appropriate experimental units and for creating aggregate variables on those units. We show that applying the resulting designs can identify important causal dependencies in real domains, and we provide examples from academic publishing, movie making and marketing, and peer-production systems. Finally, we discuss the integration of QEDs with other approaches to causal discovery, including joint modeling and directed experimentation.
Effective and efficient itemset pattern summarization: regression-based approaches
Authors: Ruoming Jin, Muad Abu-Ata, Yang Xiang, Ning Ruan
Venue: KDD
Year: 2008

In this paper, we propose a set of novel regression-based approaches to effectively and efficiently summarize frequent itemset patterns. Specifically, we show that the problem of minimizing the restoration error for a set of itemsets based on a probabilistic model corresponds to a non-linear regression problem. We show that under certain conditions, we can transform the nonlinear regression problem to a linear regression problem. We propose two new methods, k-regression and tree-regression, to partition the entire collection of frequent itemsets in order to minimize the restoration error. The K-regression approach, employing a K-means type clustering method, guarantees that the total restoration error achieves a local minimum. The tree-regression approach employs a decision-tree type of top-down partition process. In addition, we discuss alternatives to estimate the frequency for the collection of itemsets being covered by the k representative itemsets. The experimental evaluation on both real and synthetic datasets demonstrates that our approaches significantly improve the summarization performance in terms of both accuracy (restoration error), and computational cost.
Spotting out emerging artists using geo-aware analysis of P2P query strings
Authors: Noam Koenigstein, Yuval Shavitt, Tomer Tankel
Venue: KDD
Year: 2008

Record label companies would like to identify potential artists as early as possible in their careers, before other companies approach the artists with competing contracts. The vast number of candidates makes the process of identifying the ones with high success potential time consuming and laborious. This paper demonstrates how datamining of P2P query strings can be used in order to mechanize most of this detection process. Using a unique intercepting system over the Gnutella network, we were able to capture an unprecedented amount of geographically identified (geo-aware) queries, allowing us to investigate the diffusion of music related queries in time and space. Our solution is based on the observation that emerging artists, especially rappers, have a discernible stronghold of fans in their hometown area, where they are able to perform and market their music. In a file sharing network, this is reflected as a delta function spatial distribution of content queries. Using this observation, we devised a detection algorithm for emerging artists, that looks for performers with sharp increase in popularity in a small geographic region though still unnoticable nation wide. The algorithm can suggest a short list of artists with breakthrough potential, from which we showed that about 30% translate the potential to national success.
Pattern-Miner: integrated management and mining over data mining models
Authors: Evangelos E. Kotsifakos, Irene Ntoutsi, Yannis Vrahoritis, Yannis Theodoridis
Venue: KDD
Year: 2008

This demo presents Pattern-Miner, an integrated environment for pattern management and mining that deals with the whole lifecycle of patterns, from their generation (using data mining techniques) to their storage and querying, putting also emphasis on the comparison between patterns and meta-mining operations over the extracted patterns. Pattern comparison (comparing results of the data mining process) and meta-mining are high level pattern operations that can be applied in a variety of applications, from database change management to image comparison and retrieval.
Angle-based outlier detection in high-dimensional data
Authors: Hans-Peter Kriegel, Matthias Schubert, Arthur Zimek
Venue: KDD
Year: 2008

Detecting outliers in a large set of data objects is a major data mining task aiming at finding different mechanisms responsible for different groups of objects in a data set. All existing approaches, however, are based on an assessment of distances (sometimes indirectly by assuming certain distributions) in the full-dimensional Euclidean data space. In high-dimensional data, these approaches are bound to deteriorate due to the notorious "curse of dimensionality". In this paper, we propose a novel approach named ABOD (Angle-Based Outlier Detection) and some variants assessing the variance in the angles between the difference vectors of a point to the other points. This way, the effects of the "curse of dimensionality" are alleviated compared to purely distance-based approaches. A main advantage of our new approach is that our method does not rely on any parameter selection influencing the quality of the achieved ranking. In a thorough experimental evaluation, we compare ABOD to the well-established distance-based method LOF for various artificial and a real world data set and show ABOD to perform especially well on high-dimensional data.
Stream prediction using a generative model based on frequent episodes in event sequences
Authors: Srivatsan Laxman, Vikram Tankasali, Ryen W. White
Venue: KDD
Year: 2008

This paper presents a new algorithm for sequence prediction over long categorical event streams. The input to the algorithm is a set of target event types whose occurrences we wish to predict. The algorithm examines windows of events that precede occurrences of the target event types in historical data. The set of significant frequent episodes associated with each target event type is obtained based on formal connections between frequent episodes and Hidden Markov Models (HMMs). Each significant episode is associated with a specialized HMM, and a mixture of such HMMs is estimated for every target event type. The likelihoods of the current window of events, under these mixture models, are used to predict future occurrences of target events in the data. The only user-defined model parameter in the algorithm is the length of the windows of events used during model estimation. We first evaluate the algorithm on synthetic data that was generated by embedding (in varying levels of noise) patterns which are preselected to characterize occurrences of target events. We then present an application of the algorithm for predicting targeted user-behaviors from large volumes of anonymous search session interaction logs from a commercially-deployed web browser tool-bar.
Cut-and-stitch: efficient parallel learning of linear dynamical systems on smps
Authors: Lei Li, Wenjie Fu, Fan Guo, Todd C. Mowry, Christos Faloutsos
Venue: KDD
Year: 2008

Multi-core processors with ever increasing number of cores per chip are becoming prevalent in modern parallel computing. Our goal is to make use of the multi-core as well as multi-processor architectures to speed up data mining algorithms. Specifically, we present a parallel algorithm for approximate learning of Linear Dynamical Systems (LDS), also known as Kalman Filters (KF). LDSs are widely used in time series analysis such as motion capture modeling, visual tracking etc. We propose Cut-And-Stitch (CAS), a novel method to handle the data dependencies from the chain structure of hidden variables in LDS, so as to parallelize the EM-based parameter learning algorithm. We implement the algorithm using OpenMP on both a supercomputer and a quad-core commercial desktop. The experimental results show that parallel algorithms using Cut-And-Stitch achieve comparable accuracy and almost linear speedups over the serial version. In addition, Cut-And-Stitch can be generalized to other models with similar linear structures such as Hidden Markov Models (HMM) and Switching Kalman Filters (SKF).
Mining multi-faceted overviews of arbitrary topics in a text collection
Authors: Xu Ling, Qiaozhu Mei, ChengXiang Zhai, Bruce R. Schatz
Venue: KDD
Year: 2008

A common task in many text mining applications is to generate a multi-faceted overview of a topic in a text collection. Such an overview not only directly serves as an informative summary of the topic, but also provides a detailed view of navigation to different facets of the topic. Existing work has cast this problem as a categorization problem and requires training examples for each facet. This has three limitations: (1) All facets are predefined, which may not fit the need of a particular user. (2) Training examples for each facet are often unavailable. (3) Such an approach only works for a predefined type of topics. In this paper, we break these limitations and study a more realistic new setup of the problem, in which we would allow a user to flexibly describe each facet with keywords for an arbitrary topic and attempt to mine a multi-faceted overview in an unsupervised way. We attempt a probabilistic approach to solve this problem. Empirical experiments on different genres of text data show that our approach can effectively generate a multi-faceted overview for arbitrary topics; the generated overviews are comparable with those generated by supervised methods with training examples. They are also more informative than unstructured flat summaries. The method is quite general, thus can be applied to multiple text mining tasks in different application domains.
CRO: a system for online review structurization
Authors: Hongyan Liu, Hui Yang, Wenbo Li, Wei Wei, Jun He, Xiaoyong Du
Venue: KDD
Year: 2008

In this paper, we present a system called CRO (Chinese Review Observer) for online product review structurization. By Structurization, we mean identifying, extracting and summarizing information from unstructured review text to a structured table. The core tasks include review collection, product feature and user opinion extraction, and polarity analysis of opinions. Existing research in this area is mainly English text oriented. To deal with Chinese effectively, we propose several novel approaches for fulfilling the core tasks. Then we integrated these approaches and implement the whole procedure of review structurization in the system CRO. Running results for reviews of real products show its performance is satisfactory.
Multi-class cost-sensitive boosting with p-norm loss functions
Authors: Aurélie C. Lozano, Naoki Abe
Venue: KDD
Year: 2008

We propose a family of novel cost-sensitive boosting methods for multi-class classification by applying the theory of gradient boosting to p-norm based cost functionals. We establish theoretical guarantees including proof of convergence and convergence rates for the proposed methods. Our theoretical treatment provides interpretations for some of the existing algorithms in terms of the proposed family, including a generalization of the costing algorithm, DSE and GBSE-t, and the Average Cost method. We also experimentally evaluate the performance of our new algorithms against existing methods of cost sensitive boosting, including AdaCost, CSB2, and AdaBoost.M2 with cost-sensitive weight initialization. We show that our proposed scheme generally achieves superior results in terms of cost minimization and, with the use of higher order p-norm loss in certain cases, consistently outperforms the comparison methods, thus establishing its empirical advantage.
On updates that constrain the features' connections during learning
Authors: Omid Madani, Jian Huang
Venue: KDD
Year: 2008

In many multiclass learning scenarios, the number of classes is relatively large (thousands,...), or the space and time efficiency of the learning system can be crucial. We investigate two online update techniques especially suited to such problems. These updates share a sparsity preservation capacity: they allow for constraining the number of prediction connections that each feature can make. We show that one method, exponential moving average, is solving a "discrete" regression problem for each feature, changing the weights in the direction of minimizing the quadratic loss. We design the other method to improve a hinge loss subject to constraints, for better accuracy. We empirically explore the methods, and compare performance to previous indexing techniques, developed with the same goals, as well as other online algorithms based on prototype learning. We observe that while the classification accuracies are very promising, improving over previous indexing techniques, the scalability benefits are preserved.
The future of image search
Authors: Jitendra Malik
Venue: KDD
Year: 2008

There are billions of images on the Internet. Today, searching for a desired image is largely based on textual data such as filename or associated text on the web page; not much use is made of the image content. There are good reasons for this. The field of content-based image retrieval, which emerged during the 1990s, focused primarily on color and texture cues. These were easier to model than shape, but they turned out to be much less useful than originally hoped. I shall review some of the recent developments in the field of visual object recognition in the computer vision community that offer greater promise. Much better image features for characterizing shape, advances in machine learning techniques, and the availability of large amounts of training data lie at the heart of these approaches.
Weighted graphs and disconnected components: patterns and a generator
Authors: Mary McGlohon, Leman Akoglu, Christos Faloutsos
Venue: KDD
Year: 2008

The vast majority of earlier work has focused on graphs which are both connected (typically by ignoring all but the giant connected component), and unweighted. Here we study numerous, real, weighted graphs, and report surprising discoveries on the way in which new nodes join and form links in a social network. The motivating questions were the following: How do connected components in a graph form and change over time? What happens after new nodes join a network -- how common are repeated edges? We study numerous diverse, real graphs (citation networks, networks in social media, internet traffic, and others); and make the following contributions: (a) we observe that the non-giant connected components seem to stabilize in size, (b) we observe the weights on the edges follow several power laws with surprising exponents, and (c) we propose an intuitive, generative model for graph growth that obeys observed patterns.
Can complex network metrics predict the behavior of NBA teams?
Authors: Pedro O. S. Vaz de Melo, Virgílio A. F. Almeida, Antonio Alfredo Ferreira Loureiro
Venue: KDD
Year: 2008

The United States National Basketball Association (NBA) is one of the most popular sports league in the world and is well known for moving a millionary betting market that uses the countless statistical data generated after each game to feed the wagers. This leads to the existence of a rich historical database that motivates us to discover implicit knowledge in it. In this paper, we use complex network statistics to analyze the NBA database in order to create models to represent the behavior of teams in the NBA. Results of complex network-based models are compared with box score statistics, such as points, rebounds and assists per game. We show the box score statistics play a significant role for only a small fraction of the players in the league. We then propose new models for predicting a team success based on complex network metrics, such as clustering coefficient and node degree. Complex network-based models present good results when compared to box score statistics, which underscore the importance of capturing network relationships in a community such as the NBA.
Customer targeting models using actively-selected web content
Authors: Prem Melville, Saharon Rosset, Richard D. Lawrence
Venue: KDD
Year: 2008

We consider the problem of predicting the likelihood that a company will purchase a new product from a seller. The statistical models we have developed at IBM for this purpose rely on historical transaction data coupled with structured firmographic information like the company revenue, number of employees and so on. In this paper, we extend this methodology to include additional text-based features based on analysis of the content on each company's website. Empirical results demonstrate that incorporating such web content can significantly improve customer targeting. Furthermore, we present methods to actively select only the web content that is likely to improve our models, while reducing the costs of acquisition and processing.