Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- ArticleNovember 2005
Mining Patterns That Respond to Actions
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 669–672https://doi.org/10.1109/ICDM.2005.99Data mining focuses on patterns that summarize the data. In this paper, we focus on mining patterns that could change the state by responding to opportunities of actions.
- ArticleNovember 2005
Mining Patterns of Change in Remote Sensing Image Databases
- Marcelino Pereira S. Silva,
- Gilberto Camara,
- Ricardo Cartaxo M. Souza,
- Dalton M. Valeriano,
- Maria Isabel S. Escada
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 362–369https://doi.org/10.1109/ICDM.2005.98Remote sensing image databases are the fastest growing archives of spatial information. However, we still have a limited capacity for extracting information from large remote sensing image databases. There are currently very few techniques for image ...
- ArticleNovember 2005
Mining Ontological Knowledge from Domain-Specific Text Documents
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 665–668https://doi.org/10.1109/ICDM.2005.97Traditional text mining systems employ shallow parsing techniques and focus on concept extraction and taxonomic relation extraction. This paper presents a novel system called CRCTOL for mining rich semantic knowledge in the form of ontology from domain-...
- ArticleNovember 2005
Mining Minimal Distinguishing Subsequence Patterns with Gap Constraints
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 194–201https://doi.org/10.1109/ICDM.2005.96Discovering contrasts between collections of data is an important task in data mining. In this paper, we introduce a new type of contrast pattern, called a Minimal Distinguishing Subsequence (MDS). An MDS is a minimal subsequence that occurs frequently ...
- ArticleNovember 2005
Mining Frequent Spatio-Temporal Sequential Patterns
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 82–89https://doi.org/10.1109/ICDM.2005.95Many applications track the movement of mobile objects, which can be represented as sequences of timestamped locations. Given such a spatio-temporal series, we study the problem of discovering sequential patterns, which are routes frequently followed by ...
-
- ArticleNovember 2005
Mining Chains of Relations
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 553–556https://doi.org/10.1109/ICDM.2005.94Traditional data mining applications consider the problem of mining a single relation between two attributes. For example, in a scientific bibliography database, authors are related to papers, and we may be interested in discovering association rules ...
- ArticleNovember 2005
Mining Approximate Frequent Itemsets from Noisy Data
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 721–724https://doi.org/10.1109/ICDM.2005.93Frequent itemset mining is a popular and important first step in analyzing data sets across a broad range of applications. The traditional, "exact" approach for finding frequent itemsets requires that every item in the itemset occurs in each supporting ...
- ArticleNovember 2005
Merging Interface Schemas on the Deep Web via Clustering Aggregation
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 801–804https://doi.org/10.1109/ICDM.2005.92We consider the problem of integrating a large number of interface schemas over the Deep Web, The scale of the problem and the diversity of the sources present serious challenges to the conventional manual or rule-based approaches to schema integration. ...
- ArticleNovember 2005
Making Subsequence Time Series Clustering Meaningful
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 114–121https://doi.org/10.1109/ICDM.2005.91Recently, the startling claim was made that sequential time series clustering is meaningless. This has important consequences for a significant amount of work in the literature, since such a claim invalidates this work's contribution. In this paper, we ...
- ArticleNovember 2005
Making Logistic Regression a Core Data Mining Tool with TR-IRLS
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 685–688https://doi.org/10.1109/ICDM.2005.90Binary classification is a core data mining task. For large datasets or real-time applications, desirable classifiersare accurate, fast, and need no parameter tuning. We present a simple implementation of logistic regression that meets these ...
- ArticleNovember 2005
A Levelwise Search Algorithm for Interesting Subspace Clusters
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 573–576https://doi.org/10.1109/ICDM.2005.9We present a levelwise search algorithm for finding subspace clusters in high dimensional data satisfying various properties besides the commonly used minimum density property. A set of such properties are summarized and a user can choose any of these ...
- ArticleNovember 2005
Leveraging Relational Autocorrelation with Latent Group Models
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 322–329https://doi.org/10.1109/ICDM.2005.89The presence of autocorrelation provides a strong motivation for using relational learning and inference techniques. Autocorrelation is a statistical dependence between the values of the same variable on related entities and is a nearly ubiquitous ...
- ArticleNovember 2005
Learning through Changes: An Empirical Study of Dynamic Behaviors of Probability Estimation Trees
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 817–820https://doi.org/10.1109/ICDM.2005.88In practice, learning from data is often hampered by the limited training examples. In this paper, as the size of training data varies, we empirically investigate several probability estimation tree algorithms over eighteen binary classification ...
- ArticleNovember 2005
Learning Instance Greedily Cloning Naive Bayes for Ranking
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 202–209https://doi.org/10.1109/ICDM.2005.87Naive Bayes (simply NB) [12] has been widely used in machine learning and data mining as a simple and effective classification algorithm. Since its conditional independence assumption is rarely true, researchers have made a substantial amount of effort ...
- ArticleNovember 2005
Learning Functional Dependency Networks Based on Genetic Programming
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 394–401https://doi.org/10.1109/ICDM.2005.86Bayesian Network (BN) is a powerful network model, which represents a set of variables in the domain and provides the probabilistic relationships among them. But BN can handle discrete values only; it cannot handle continuous, interval and ordinal ones, ...
- ArticleNovember 2005
Labeling Unclustered Categorical Data into Clusters Based on the Important Attribute Values
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 106–113https://doi.org/10.1109/ICDM.2005.85Sampling has been recognized as an important technique to improve the efficiency of clustering. However, with sampling applied, those points which are not sampled will not have their labels. Although there is a straightforward approach in the numerical ...
- ArticleNovember 2005
Kernel-Density-Based Clustering of Time Series Subsequences Using a Continuous Random-Walk Noise Model
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 122–129https://doi.org/10.1109/ICDM.2005.84Noise levels in time series subsequence data are typically very high, and properties of the noise differ from those of white noise. The proposed algorithm incorporates a continuous random-walk noise model into kernel-density-based clustering. Evaluation ...
- ArticleNovember 2005
Integrating Hidden Markov Models and Spectral Analysis for Sensory Time Series Clustering
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 506–513https://doi.org/10.1109/ICDM.2005.82We present a novel approach for clustering sequences of multi-dimensional trajectory data obtained from a sensor network. The sensory time-series data present new challenges to data mining, including uneven sequence lengths, multi-dimensionality and ...
- ArticleNovember 2005
Instability of Classifiers on Categorical Data
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 769–772https://doi.org/10.1109/ICDM.2005.81In this paper we study the local behaviour of arbitrary classifiers using the instability of that classifier in a data point. Moreover, we introduce two algorithms. The first to find highly unstable points, the second to find islands of stability.
- ArticleNovember 2005
Improving Automatic Query Classification via Semi-Supervised Learning
ICDM '05: Proceedings of the Fifth IEEE International Conference on Data MiningNovember 2005, Pages 42–49https://doi.org/10.1109/ICDM.2005.80Accurate topical classification of user queries allows for increased effectiveness and efficiency in general-purpose web search systems. Such classification becomes critical if the system is to return results not just from a general web collection but ...