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1,265
Human DNA methylomes at base resolution show widespread epigenomic differences.
- Nature,
, 2009
"... DNA cytosine methylation is a central epigenetic modification that has essential roles in cellular processes including genome regulation, development and disease. Here we present the first genome-wide, single-base-resolution maps of methylated cytosines in a mammalian genome, from both human embryo ..."
Abstract
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Cited by 401 (6 self)
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DNA cytosine methylation is a central epigenetic modification that has essential roles in cellular processes including genome regulation, development and disease. Here we present the first genome-wide, single-base-resolution maps of methylated cytosines in a mammalian genome, from both human
Discovery of spatial association rules in geographic information databases
, 1995
"... Abstract. Spatial data mining, i.e., discovery of interesting, implicit knowledge in spatial databases, is an important task for understanding and use of spatial data- and knowledge-bases. In this paper, an e cient method for mining strong spatial association rules in geographic information database ..."
Abstract
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Cited by 212 (14 self)
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implication relationships. Several optimization techniques are explored, including a two-step spatial computation technique (approximate computation on large sets, and re ned computations on small promising patterns), shared processing in the derivation of large predicates at multiple concept levels, etc. Our
Discovery-driven Exploration of OLAP Data Cubes
- In Proc. Int. Conf. of Extending Database Technology (EDBT'98
, 1998
"... . Analysts predominantly use OLAP data cubes to identify regions of anomalies that may represent problem areas or new opportunities. The current OLAP systems support hypothesis-driven exploration of data cubes through operations such as drill-down, roll-up, and selection. Using these operations, an ..."
Abstract
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Cited by 110 (2 self)
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, an analyst navigates unaided through a huge search space looking at large number of values to spot exceptions. We propose a new discovery-driven exploration paradigm that mines the data for such exceptions and summarizes the exceptions at appropriate levels in advance. It then uses these exceptions to lead
Probabilistic Author-Topic Models for Information Discovery
- THE TENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING
, 2004
"... We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probabilit ..."
Abstract
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Cited by 173 (11 self)
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We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a
Knowledge Discovery in Textual Databases (KDT)
- In Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95
, 1995
"... The information age is characterized by a rapid growth in the amount of information available in electronic media. Traditional data handling methods are not adequate to cope with this information flood. Knowledge Discovery in Databases (KDD) is a new paradigm that focuses on computerized exploration ..."
Abstract
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Cited by 113 (2 self)
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exploration of large amounts of data and on discovery of relevant and interesting patterns within them. While most work on KDD is concerned with structured databases, it is clear that this paradigm is required for handling the huge amount of information that is available only in unstructured textual form
Exploration of Simulation Experiments by Discovery
, 1994
"... : We exemplify in this paper, how a discovery system is applied to the analysis of simulation experiments in practical political planning, and show what kind of new knowledge can be discovered in an application area that differs from others by the high amount of knowledge that the analyst holds alre ..."
Abstract
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Cited by 3 (1 self)
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the user to adapt the discovery process to the special requirements of the application. The combination of discovery with simulation is endowed with the main characteristics of both Knowledge Discovery in Databases (KDD) and Automated Scientific Discovery (ASD), i.e. discovery in large databases
Automating Process Discovery through Event-Data Analysis
- IN PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING
, 1995
"... Many software process methods and tools presuppose the existence of a formal model of a process. Unfortunately, developing a formal model for an on-going, complex process can be difficult, costly, and error prone. This presents a practical barrier to the adoption of process technologies. The barrier ..."
Abstract
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Cited by 82 (5 self)
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. The barrier would be lowered by automating the creation of formal models. We are currently exploring techniques that can use basic event data captured from an on-going process to generate a formal model of process behavior. We term this kind of data analysis process discovery. This paper describes
Exploration of Simulation Experiments by Discovery
"... Abstract: We exemplify in this paper, how a discovery system is applied to the analysis of simulation experiments in practical political planning, and show what kind of new knowledge can be discovered in an application area that differs from others by the high amount of knowledge that the analyst ho ..."
Abstract
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, allowing the user to adapt the discovery process to the special requirements of the application. The combination of discovery with simulation is endowed with the main characteristics of both Knowledge Discovery in Databases (KDD) and Automated Scientific Discovery (ASD), discovery in large databases
Clustering by Pattern Similarity in Large Data Sets
- In SIGMOD
"... Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model to another, in most of these models the concept of similarity is based on distances, e.g., Euclidean distance or cosine distance. In other words, ..."
Abstract
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Cited by 182 (19 self)
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Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model to another, in most of these models the concept of similarity is based on distances, e.g., Euclidean distance or cosine distance. In other words
CrimeNet explorer: a framework for criminal network knowledge discovery
- ACM Transactions on Information Systems (TOIS
, 2005
"... Knowledge about the structure and organization of criminal networks is important for both crime investigation and the development of effective strategies to prevent crimes. However, except for network visualization, criminal network analysis remains primarily a manual process. Existing tools do not ..."
Abstract
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Cited by 43 (7 self)
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Knowledge about the structure and organization of criminal networks is important for both crime investigation and the development of effective strategies to prevent crimes. However, except for network visualization, criminal network analysis remains primarily a manual process. Existing tools do
Results 1 - 10
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1,265
