We use Text Mining, Deep Learning and Big Data Analytics to unleash the potential of unstructured data and to integrate unused assets into decision-making processes.
Kaggle is a platform for data prediction competitions. Companies, organizations and researchers post their data and have it scrutinized by the world's best statisticians.
SUBDUE is a graph-based knowledge discovery system that finds structural, relational patterns in data representing entities and relationships. SUBDUE represents data using a labeled, directed graph in which entities are represented by labeled vertices or subgraphs, and relationships are represented by labeled edges between the entities. SUBDUE uses the minimum description length (MDL) principle to identify patterns that minimize the number of bits needed to describe the input graph after being compressed by the pattern. SUBDUE can perform several learning tasks, including unsupervised learning, supervised learning, clustering and graph grammar learning.
The workshop aims to discuss key issues and practices of semantic mining. Thanks to the initiatives of the Linked Open Data and robust techniques for semantic annotation of Web, social, and sensor data, more semantic data is available. Many research efforts have been directed toward demonstrating semantic techniques to analyze and mine this growing resource. The workshop will provide a cross-disciplinary forum for researchers to showcase their innovation and efforts, and to further enhance existing bounds and create new connections among different communities. Here we solicit contributions on researches and practices of mining data semantics including theory, algorithms, and applications from computer science, life science, healthcare and other domains.
Open source business intelligence from Pentaho saves your business time and money. We specialize in open source reporting, open source etl & data integration and open source olap.
At the highest level of description, this book is about data mining. However,
it focuses on data mining of very large amounts of data, that is, data so large
it does not fit in main memory. Because of the emphasis on size, many of our
examples are about the Web or data derived from the Web. Further, the book
takes an algorithmic point of view: data mining is about applying algorithms
to data, rather than using data to “train” a machine-learning engine of some
sort.
A java-based framework for index-structure supported knowledge discovery and data mining algorithms with a fundamental approach to separate data management (file parsers, database connections, data types) and algorithms (distances, distance functions, and data mining algorithms).
Mloss is a community effort at producing reproducible research via open source software, open access to data and results, and open standards for interchange.
forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online
"Very large data sets often have a flat but regular structure and span multiple disks and machines. ... These large data sets are not amenable to study using traditional dat
An AOL researcher inadverntly released a database of 21 million search queries to the Web. It's been snagged and is providing a valuable trove of sociological search behavior ... and potential privacy intrusion if the ID numbers are ever associated with u
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