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Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on

Date March 1 2007-April 5 2007

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Displaying Results 1 - 25 of 113
  • General CIDM Co-chairs' Welcome Letter

    Publication Year: 2007, Page(s): nil1
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    Freely Available from IEEE
  • Program Committee

    Publication Year: 2007, Page(s):nil2 - nil3
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    Freely Available from IEEE
  • IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007)

    Publication Year: 2007, Page(s):nil4 - nil10
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    Freely Available from IEEE
  • Link Analysis of Incomplete Relationship Networks

    Publication Year: 2007, Page(s):1 - 5
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (155 KB) | HTML iconHTML

    We present a method of learning relationships at the triadic level of a relationship network. The method proposes learning linkages of a particular network using a support vector machine (SVM) classifier trained on the known part of a relationship network. Using features drawn from the topological information of the two degrees of separation of a link a classifier learns whether two people of that... View full abstract»

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  • Validity of Probabilistic Rules

    Publication Year: 2007, Page(s):6 - 9
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (104 KB) | HTML iconHTML

    We propose an axiomatic approach to defining of the validity of probabilistic inductive rules E rArr H. The set of rules is evaluated against an available dataset, where the conditions E, H are either true or false for each instance in the dataset. Introduced here are six axioms which formalize common sense dependencies between the validity of rules and their support, confidence, lift and amount o... View full abstract»

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  • An Efficient Distance Calculation Method for Uncertain Objects

    Publication Year: 2007, Page(s):10 - 17
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (348 KB) | HTML iconHTML

    Recently the academic communities have paid more attention to the queries and mining on uncertain data. In the tasks such as clustering or nearest-neighbor queries, expected distance is often used as a distance measurement among uncertain data objects. Traditional database systems store uncertain objects using their expected (average) location in the data space. Distances can be calculated easily ... View full abstract»

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  • K2GA: Heuristically Guided Evolution of Bayesian Network Structures from Data

    Publication Year: 2007, Page(s):18 - 25
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (349 KB) | HTML iconHTML

    We present K2GA, an algorithm for learning Bayesian network structures from data. K2GA uses a genetic algorithm to perform stochastic search, while employing a modified version of the K2 heuristic to score proposed networks and improve future generations. We show each component of K2GA, a combination of these components to form the basic algorithm, extensions to the algorithm for improved accuracy... View full abstract»

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  • Extracting Borderline Associations

    Publication Year: 2007, Page(s):26 - 30
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (121 KB) | HTML iconHTML

    In this paper, we present an extension of the well known algorithm for association mining, Apriori. This extended algorithm, ApriorBL, considers associations between items which occur together - focusing solely on the borderline cases. These borderline cases occur often enough to provide valuable information; however, there are currently no algorithms that target them. We discuss how the AprioriBL... View full abstract»

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  • Selecting the Right Peer Schools for AACSB Accreditation - A Data Mining Application

    Publication Year: 2007, Page(s):31 - 34
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (75 KB) | HTML iconHTML

    For a business school, the selection of its peer schools is an important component of its International Association for Management Education (AACSB) (re)accreditation process. A school typically compares itself with other institutions having similar structural and identity-based attributes. The identification of peer schools is critical and can have a significant impact on a business school's accr... View full abstract»

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  • Structure Prediction in Temporal Networks using Frequent Subgraphs

    Publication Year: 2007, Page(s):35 - 42
    Cited by:  Papers (8)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (201 KB) | HTML iconHTML

    There are several types of processes which can be modeled explicitly by recording the interactions between a set of actors over time. In such applications, a common objective is, given a series of observations, to predict exactly when certain interactions will occur in the future. We propose a representation for this type of temporal data and a generic, streaming, adaptive algorithm to predict the... View full abstract»

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  • An Analytical Evaluation of Objective Measures Behavior for Generalized Association Rules

    Publication Year: 2007, Page(s):43 - 50
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (7383 KB) | HTML iconHTML

    The association rule mining task identifies all the intrinsic associations among the items contained in data and leads to only specialized knowledge. To overcome this problem the generalized association rules appeared. This type of rule associates not only the items contained in data, but also some items encoded into a given taxonomy. Therefore, the techniques used to obtain generalized associatio... View full abstract»

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  • Versatile and Efficient Meta-Learning Architecture: Knowledge Representation and Management in Computational Intelligence

    Publication Year: 2007, Page(s):51 - 58
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (194 KB) | HTML iconHTML

    There are many data mining systems derived from machine learning, neural network, statistics and other fields. Most of them are dedicated to some particular algorithms or applications. Unfortunately, their architectures are still too naive to provide satisfactory background for advanced meta-learning problems. In order to efficiently perform sophisticated meta-level analysis, we need a very versat... View full abstract»

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  • Query-sensitive Feature Selection for Lazy Learners

    Publication Year: 2007, Page(s):59 - 65
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (205 KB) | HTML iconHTML

    Feature selection contributes to increasing many learners' accuracy by identifying and removing irrelevant features in multidimensional datasets. Conventional feature selection methods determine the optimal feature subset independently from and prior to the introduction of a new query. In general, some features will be relevant only in certain tasks. We argue that a query, as an indicator of the a... View full abstract»

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  • Comparison of Classifiers Efficiency on Missing Values Recovering: Application in a Marketing Database with Massive Missing Data

    Publication Year: 2007, Page(s):66 - 72
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (233 KB) | HTML iconHTML

    Missing data in databases are considered to be one of the biggest problems faced on data mining application. This problem can be aggravated when there is massive missing data in the presence of imbalanced databases. Several techniques as samples deletion, values imputation, values prediction through classifiers and approximation of patterns have been proposed and compared, but these comparisons do... View full abstract»

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  • Manifold Learning using Growing Locally Linear Embedding

    Publication Year: 2007, Page(s):73 - 80
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (971 KB) | HTML iconHTML

    Locally linear embedding (LLE) is an effective nonlinear dimensionality reduction method for exploring the intrinsic characteristics of high dimensional data. This paper mainly proposes a hierarchical framework manifold learning method, based on LLE and growing neural gas (GNG), named growing locally linear embedding (GLLE). First, we address the major limitations of the original LLE: intrinsic di... View full abstract»

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  • A Novel Complex-Valued Counterpropagation Network

    Publication Year: 2007, Page(s):81 - 87
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (6860 KB) | HTML iconHTML

    The counterpropagation network is a combination of competitive network (Kohonen layer) and Grossberg outstar structure. In this paper we have proposed a complex valued representation on conventional forward only counterpropagation network. Many researchers have investigated the computational capabilities of neuron models for real values only. The novel part of the paper is, while considering the c... View full abstract»

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  • A Prototype-driven Framework for Change Detection in Data Stream Classification

    Publication Year: 2007, Page(s):88 - 95
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (8728 KB) | HTML iconHTML

    This paper presents a prototype-driven framework for classifying evolving data streams. Our framework uses cluster prototypes to summarize the data and to determine whether the current model is outdated. This strategy of rebuilding the model only when significant changes are detected helps to reduce the computational overhead and the amount of labeled examples needed. To improve its accuracy, we a... View full abstract»

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  • Evolutionary Optimization of Three-Photon Absorption in Molecular Iodine

    Publication Year: 2007, Page(s):96 - 100
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (6296 KB) | HTML iconHTML

    We report on the application of an evolutionary algorithm to a noisy, dynamic optimization problem in chemistry: the maximization of three-photon absorption in molecular iodine. An evolution strategy is used in real-time in a closed loop experiment to search the space of physically realizable phase-modulated femtosecond laser pulses. The probability of three-photon absorption is estimated by measu... View full abstract»

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  • Induction Tree methods to classify M. tuberculosis spoligotypes

    Publication Year: 2007, Page(s):101 - 106
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (152 KB) | HTML iconHTML

    In this paper we compared and analyzed four graph induction methods to automatically classify spoligotypes. A spoligotype is a sequence of 43 binary values provided by a DNA analysis technique. This method is known to be useful and efficient to many supervised learning problems. We found it interesting to use these techniques especially for sequential data, in order to create a classifier based on... View full abstract»

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  • Data Clustering and Fuzzy Neural Network for Sales Forecasting in Printed Circuit Board Industry

    Publication Year: 2007, Page(s):107 - 113
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (6511 KB) | HTML iconHTML

    Reliable prediction of sales can improve the quality of business strategy. This research develops a hybrid model by integrating K-mean cluster and fuzzy back propagation network (KFBPN) to forecast the future sales of a printed circuit board factory. Based on the K-mean clustering technique, the historic data can be classified into different clusters, thus the noise of the original data can be red... View full abstract»

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  • Fuzzy Wavelet Modeling Using Data Clustering

    Publication Year: 2007, Page(s):114 - 119
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (5211 KB) | HTML iconHTML

    In this paper, a novel approach for tuning the parameters of fuzzy wavelet systems which are used for modeling of nonlinear and complex systems is proposed. In fuzzy inference system, each fuzzy rule is analogous to a wavelet basis function multiplied by a coefficient. Using clustering techniques, the center of these basis functions are located in the detected center of clusters. In this way, not ... View full abstract»

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  • SSM : A Frequent Sequential Data Stream Patterns Miner

    Publication Year: 2007, Page(s):120 - 126
    Cited by:  Papers (2)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (262 KB) | HTML iconHTML

    Data stream applications like sensor network data, click stream data, have data arriving continuously at high speed rates and require online mining process capable of delivering current and near accurate results on demand without full access to all historical stored data. Frequent sequential mining is the process of discovering frequent sequential patterns in data sequences as found in application... View full abstract»

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  • 0-SM: A fast algorithm for mining Candidate Clusters in Pattern-based Clustering

    Publication Year: 2007, Page(s):127 - 132
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (270 KB) | HTML iconHTML

    Unlike traditional clustering methods that focus on grouping objects with similar values on a set of dimensions, clustering by pattern similarity finds objects that exhibit a coherent pattern of rise and fall in subspaces. Pattern-based clustering extends the concept of traditional clustering and benefits a wide range of applications, including large scale scientific data analysis, target marketin... View full abstract»

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  • Cluster Detection with the PYRAMID Algorithm

    Publication Year: 2007, Page(s):133 - 138
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (502 KB) | HTML iconHTML

    As databases continue to grow in size, efficient and effective clustering algorithms play a paramount role in data mining applications. Practical clustering faces several challenges including: identifying clusters of arbitrary shapes, sensitivity to the order of input, dynamic determination of the number of clusters, outlier handling, processing speed of massive data sets, handling higher dimensio... View full abstract»

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  • Resource-aware Online Data Mining in Wireless Sensor Networks

    Publication Year: 2007, Page(s):139 - 146
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (569 KB) | HTML iconHTML

    Data processing in wireless sensor networks often relies on high-speed data stream input, but at the same time is inherently constrained by limited resource availability. Thus, energy efficiency and good resource management are vital for in-network processing techniques. We propose enabling resource-awareness for in-network processing algorithms by means of a resource monitoring component and desi... View full abstract»

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