## Proceedings of the Fourth SIAM International Conference on Data MiningThe Fourth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. This is reflected in the talks by the four keynote speakers who discuss data usability issues in systems for data mining in science and engineering, issues raised by new technologies that generate biological data, ways to find complex structured patterns in linked data, and advances in Bayesian inference techniques. This proceedings includes 61 research papers. |

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### Contents

A New Model for Clustering Lineor Sequences | 23 |

Nonlinedr Monifold Leorning for Doto Stream | 33 |

39 Visuolizing RFM Segmentotion | 39 |

Text Mining from Site Invoriont ond Dependent Fedtures for Information Extraction | 45 |

Contents | 49 |

Constructing Time Decompositions for Andlyzing Time Stomped Documents | 57 |

Equivalence of Severol TwoStage Methods for Linedr Discriminont Andlysis | 69 |

A Fromework for Discovering Colocation Potterns in Doto Sets with Extended Spotid | 78 |

Clustering Cotegoricol Doto Using the CorrelatedForce Ensemble | 269 |

A Mobile ond Distributed Doto Streom Mining System for RedTime Vehicle | 300 |

Active SemiSupervision for Poirwise Constroined Clustering | 333 |

A Generol Probobilistic Fromework for Mining Lobeled Ordered Trees | 357 |

A Mixture Model for Clustering Ensembles | 379 |

Visually Mining through Cluster Hierorchies | 400 |

ClossSpecific Ensembles for Active Leorning in Digital Imogery | 412 |

Mining Text for Word Senses Using Independent Component Andlysis | 422 |

Using Support Vector Mochines for Clossifying Lorge Sets of MultiRepresented Objects | 102 |

Minimum SumSqudred Residue CoClustering of Gene Expression Doto | 114 |

Troining Support Vector Mochine Using Adoptive Clustering | 126 |

REP++ A Foster Rule Leorning Algorithm | 138 |

A Single Poss Generolized Incrementol Algorithm for Clustering | 147 |

A Distributed Tool for Constructing Summories of HighDimensional Discrete | 154 |

Bosic ASSociotion Rules | 166 |

Hierorchicol Clustering for Themotic Browsing ond Summorizotion of Lorge Sets | 178 |

An Abstract Weighting Fromework for Clustering Algorithms | 200 |

Lineor Regression ond Clossificotion | 222 |

DensityConnected SubSpoce Clustering for HighDimensiond Doto | 246 |

BAMBOO Acceleroting Closed itemset Mining by Deeply Pushing the LengthDecredsing | 432 |

A Recursive Model for Groph Mining | 442 |

Text Mining Using Nonnegotive Matrix Foctorizotions | 452 |

The Aspect Bernoulli Model | 462 |

FD Iterotive Fedture ond Doto Clustering | 472 |

A Foundationol Approach to Mining itemset Utilities from Dotoboses | 482 |

ReservoirBosed Rondom Sompling with Replacement from Doto Stream | 492 |

Clossifying Documents without Lobels | 502 |

Subspace Clustering of High Dimensional Doto | 517 |

Mining Potters of Activity from Video Doto | 532 |

### Common terms and phrases

accuracy analysis applied approach association rules binary centers centroid classification clustering algorithm ClusterSVM co-clustering co-location patterns coarse-level column computed concept graph conditional probability constraints contains corresponding Data Mining data set database defined denoted distance distribution document set efficient Euclidean distance evaluation extraction feature genes given hierarchical IEKA initial input International Conference interval IREP++ ISOMAP iteration k-means k-means algorithm kernel keywords Knowledge Discovery labeled LDA/GSVD Lemma linear Machine Learning matrix maximal measure function method minimal minimum support Mixture Density money money money node number of subintervals number of training objective function obtained ontologies optimal information preserving parameters partition performance points problem Proc protein pruning random representation RIPPER Section sequence space spatial structure subgraph subset subspace support vector machines Table techniques Teiresias text fragments text mining tion ToMMS training data training examples tree UPGMA wrapper