PKDD 2004 - PDF Preprint Papers

PKDD 2004 European Symposium/Conference on Principles of Data Mining and Knowledge Discovery

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PKDD
2004
Springer

324views Data Mining» more PKDD 2004»

Orange: From Experimental Machine Learning to Interactive Data Mining

9 years 5 months ago

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Abstract. Orange (www.ailab.si/orange) is a suite for machine learning and data mining. It can be used though scripting in Python or with visual programming in Orange Canvas using ...

Janez Demsar, Blaz Zupan, Gregor Leban, Tomaz Curk

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Id	Views	Title	Status

1	324	Orange: From Experimental Machine Learning to Interactive Data Mining	claim
2	277	Scalable Density-Based Distributed Clustering	claim
3	205	Breaking Through the Syntax Barrier: Searching with Entities and Relations	claim
4	199	SemanticTalk: Software for Visualizing Brainstorming Sessions and Thematic Concept Trails on Document Collections	claim
5	168	Combining Winnow and Orthogonal Sparse Bigrams for Incremental Spam Filtering	claim
6	166	A Tolerance Rough Set Approach to Clustering Web Search Results	claim
7	155	Ensemble Feature Ranking	claim
8	147	Using a Hash-Based Method for Apriori-Based Graph Mining	claim
9	144	SEWeP: A Web Mining System Supporting Semantic Personalization	claim
10	141	Spatial Associative Classification at Different Levels of Granularity: A Probabilistic Approach	claim
11	138	Combining Multiple Clustering Systems	claim
12	135	A Unified and Flexible Framework for Comparing Simple and Complex Patterns	claim
13	131	Asynchronous and Anticipatory Filter-Stream Based Parallel Algorithm for Frequent Itemset Mining	claim
14	118	Learning from Multi-source Data	claim
15	118	Mining Thick Skylines over Large Databases	claim
16	116	Random Matrices in Data Analysis	claim
17	114	Visual Mining of Spatial Time Series Data	claim
18	113	Discovery of Regulatory Connections in Microarray Data	claim
19	112	Discovering Interpretable Muscle Activation Patterns with the Temporal Data Mining Method	claim
20	111	SPIN! Data Mining System Based on Component Architecture	claim
21	109	Data Privacy	claim
22	105	Density-Based Spatial Clustering in the Presence of Obstacles and Facilitators	claim
23	102	Improving the Performance of the RISE Algorithm	claim
24	97	Dealing with Predictive-but-Unpredictable Attributes in Noisy Data Sources	claim
25	92	Properties and Benefits of Calibrated Classifiers	claim
26	92	Strength in Diversity: The Advance of Data Analysis	claim
27	91	Summarization of Dynamic Content in Web Collections	claim
28	90	Shape and Size Regularization in Expectation Maximization and Fuzzy Clustering	claim
29	86	Real-World Learning with Markov Logic Networks	claim
30	81	Learning from Little: Comparison of Classifiers Given Little Training	claim
31	79	A Framework for Data Mining Pattern Management	claim

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