KDD 2004 - PDF Preprint Papers

KDD 2004 International Conference on Knowledge Discovery and Data Mining

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KDD
2004
ACM

624views Data Mining» more KDD 2004»

Programming the K-means clustering algorithm in SQL

14 years 5 months ago

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Using SQL has not been considered an eﬃcient and feasible way to implement data mining algorithms. Although this is true for many data mining, machine learning and statistical a...

Carlos Ordonez

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Id	Views	Title	Status

1	624	Programming the K-means clustering algorithm in SQL	claim
2	330	Learning to detect malicious executables in the wild	claim
3	314	Assessment of discretization techniques for relevant pattern discovery from gene expression data	claim
4	302	Redundancy based feature selection for microarray data	claim
5	285	Effective localized regression for damage detection in large complex mechanical structures	claim
6	237	Bayesian Model-Averaging in Unsupervised Learning From Microarray Data	claim
7	216	GPCA: an efficient dimension reduction scheme for image compression and retrieval	claim
8	213	Analytical view of business data	claim
9	211	Towards parameter-free data mining	claim
10	210	Web usage mining based on probabilistic latent semantic analysis	claim
11	210	Visually mining and monitoring massive time series	claim
12	210	Probabilistic author-topic models for information discovery	claim
13	209	Tracking dynamics of topic trends using a finite mixture model	claim
14	209	A data mining approach to modeling relationships among categories in image collection	claim
15	207	Belief state approaches to signaling alarms in surveillance systems	claim
16	207	SPIN: mining maximal frequent subgraphs from graph databases	claim
17	198	Mining traffic data from probe-car system for travel time prediction	claim
18	196	Adversarial classification	claim
19	195	Improved robustness of signature-based near-replica detection via lexicon randomization	claim
20	192	Mining and summarizing customer reviews	claim
21	190	Kernel k-means: spectral clustering and normalized cuts	claim
22	190	V-Miner: using enhanced parallel coordinates to mine product design and test data	claim
23	187	IMMC: incremental maximum margin criterion	claim
24	187	Mission-Based Navigational Behaviour Modeling for Web Recommender Systems	claim
25	182	Rotation invariant distance measures for trajectories	claim
26	181	Column-generation boosting methods for mixture of kernels	claim
27	179	1-dimensional splines as building blocks for improving accuracy of risk outcomes models	claim
28	173	A microeconomic data mining problem: customer-oriented catalog segmentation	claim
29	173	Collaborative Quality Filtering: Establishing Consensus or Recovering Ground Truth?	claim
30	171	Integrating Web Conceptual Modeling and Web Usage Mining	claim
31	170	Why collective inference improves relational classification	claim
32	170	Estimating the size of the telephone universe: a Bayesian Mark-recapture approach	claim
33	168	Mining, indexing, and querying historical spatiotemporal data	claim
34	166	Predicting prostate cancer recurrence via maximizing the concordance index	claim
35	164	Discovering complex matchings across web query interfaces: a correlation mining approach	claim
36	164	Cluster-based concept invention for statistical relational learning	claim
37	164	Ordering patterns by combining opinions from multiple sources	claim
38	163	A Novel Approach for Prediction of Protein Subcellular Localization from Sequence Using Fourier Analysis and Support Vector Machines	claim
39	163	Exploiting dictionaries in named entity extraction: combining semi-Markov extraction processes and data integration methods	claim
40	162	Discovering Links Between Lexical and Surface Features in Questions and Answers	claim
41	161	ANN quality diagnostic models for packaging manufacturing: an industrial data mining case study	claim
42	160	k-TTP: a new privacy model for large-scale distributed environments	claim
43	160	Boosting for Text Classification with Semantic Features	claim
44	159	Optimal randomization for privacy preserving data mining	claim
45	158	A generalized maximum entropy approach to bregman co-clustering and matrix approximation	claim
46	157	On detecting space-time clusters	claim
47	157	Fast mining of spatial collocations	claim
48	156	TiVo: making show recommendations using a distributed collaborative filtering architecture	claim
49	154	Diagnosing extrapolation: tree-based density estimation	claim
50	152	The complexity of mining maximal frequent itemsets and maximal frequent patterns	claim
51	151	Feature selection in scientific applications	claim
52	150	A framework for ontology-driven subspace clustering	claim
53	150	Complete This Puzzle: A Connectionist Approach to Accurate Web Recommendations Based on a Committee of Predictors	claim
54	150	Markov Blankets and Meta-heuristics Search: Sentiment Extraction from Unstructured Texts	claim
55	148	Fast discovery of connection subgraphs	claim
56	148	Interestingness of frequent itemsets using Bayesian networks as background knowledge	claim
57	148	A quickstart in frequent structure mining can make a difference	claim
58	148	Spying Out Accurate User Preferences for Search Engine Adaptation	claim
59	147	Clustering time series from ARMA models with clipped data	claim
60	147	Semantic representation: search and mining of multimedia content	claim
61	146	Clustering Labeled Data and Cross-Validation for Classification with Few Positives in Yeast	claim
62	146	A Bayesian network framework for reject inference	claim
63	146	Using Hyperlink Features to Personalize Web Search	claim
64	145	Discovering Spatial Relationships Between Approximately Equivalent Patterns in Contact Maps	claim
65	145	Mining coherent gene clusters from gene-sample-time microarray data	claim
66	145	A graph-theoretic approach to extract storylines from search results	claim
67	144	IncSpan: incremental mining of sequential patterns in large database	claim
68	143	Incremental maintenance of quotient cube for median	claim
69	142	Meta-classification of Multi-type Cancer Gene Expression Data	claim
70	141	A Maximum Entropy Approach to Biomedical Named Entity Recognition	claim
71	139	Machine learning for online query relaxation	claim
72	139	Learning a complex metabolomic dataset using random forests and support vector machines	claim
73	138	Privacy-preserving Bayesian network structure computation on distributed heterogeneous data	claim
74	138	IDR/QR: an incremental dimension reduction algorithm via QR decomposition	claim
75	137	When do data mining results violate privacy?	claim
76	137	Mining scale-free networks using geodesic clustering	claim
77	136	Exploring the community structure of newsgroups	claim
78	136	A cross-collection mixture model for comparative text mining	claim
79	135	Cross channel optimized marketing by reinforcement learning	claim
80	135	On demand classification of data streams	claim
81	135	Discovering additive structure in black box functions	claim
82	134	Exploiting a support-based upper bound of Pearson's correlation coefficient for efficiently identifying strongly correlated pairs	claim
83	132	A probabilistic framework for semi-supervised clustering	claim
84	132	Privacy preserving regression modelling via distributed computation	claim
85	131	Fast nonlinear regression via eigenimages applied to galactic morphology	claim
86	127	A generative probabilistic approach to visualizing sets of symbolic sequences	claim
87	126	High-throughput Protein Interactome Data: Minable or Not?	claim
88	126	Efficient closed pattern mining in the presence of tough block constraints	claim
89	126	Turning CARTwheels: an alternating algorithm for mining redescriptions	claim
90	126	Dense itemsets	claim
91	126	Selection, combination, and evaluation of effective software sensors for detecting abnormal computer usage	claim
92	125	Differential Association Rule Mining for the Study of Protein-Protein Interaction Networks	claim
93	125	Graphical models for data mining	claim
94	125	A general approach to incorporate data quality matrices into data mining algorithms	claim
95	124	Eigenspace-based anomaly detection in computer systems	claim
96	124	Automatic multimedia cross-modal correlation discovery	claim
97	124	Support envelopes: a technique for exploring the structure of association patterns	claim
98	124	Incorporating prior knowledge with weighted margin support vector machines	claim
99	123	A DEA approach for model combination	claim
100	118	Parallel computation of high dimensional robust correlation and covariance matrices	claim
101	117	Systematic data selection to mine concept-drifting data streams	claim
102	117	Predicting customer shopping lists from point-of-sale purchase data	claim
103	117	Regularized multi--task learning	claim
104	116	A system for automated mapping of bill-of-materials part numbers	claim
105	115	A New Approach to Protein Structure Mining and Alignment	claim
106	114	Mining reference tables for automatic text segmentation	claim
107	114	Mining the space of graph properties	claim
108	114	Scalable mining of large disk-based graph databases	claim
109	113	Learning spatially variant dissimilarity (SVaD) measures	claim
110	112	An iterative method for multi-class cost-sensitive learning	claim
111	112	A rank sum test method for informative gene discovery	claim
112	111	Rapid detection of significant spatial clusters	claim
113	111	Document preprocessing for naive Bayes classification and clustering with mixture of multinomials	claim
114	110	Generalizing the notion of support	claim
115	109	Identifying early buyers from purchase data	claim
116	108	Sleeved coclustering	claim
117	108	Density-based spam detector	claim
118	106	Early detection of insider trading in option markets	claim
119	103	An objective evaluation criterion for clustering	claim
120	101	Clustering moving objects	claim
121	92	Recovering latent time-series from their observed sums: network tomography with particle filters	claim
122	90	The IOC algorithm: efficient many-class non-parametric classification for high-dimensional data	claim
123	89	Fully automatic cross-associations	claim

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