IVOA KDD-IG: A user guide for Data Mining in Astronomy2: Examples of improved results enabled by data mining techniquesWe describe some examples where data mining techniques allowed improved astronomical results. The list aims to be comprehensive and illustrative of the range of results, but not exhaustive, or a literature review. The emphasis is on refereed articles, or published conference proceedings. We begin with science results, including Virtual-Observatory-enabled science and the discovery of new objects, then improved utility results such as processing, object detection and classification, and photometric redshifts. The emphasis is on results enabled by KDD and astroinfomatics, although inevitably there is overlap with astrostatistics, computer science, and other fields. Broadly, the section can be summarized as a collection of examples where specific, significant improvement came from the methods, or improved access to existing data, rather than new data. However, if a result is from a combination of both new data and astroinformatics methods (e.g., the data might otherwise be intractable), then such results are also included. Papers are given in approximate chronological order, newest first. Because our aim here is not to review everything, but rather to select highlights, we provide links to online databases, e.g., the NASA Astrophysics Data system, which give a more complete overview of the full range of astronomy literature in which data mining was used. Given the rather nebulous definition of data mining, it is inevitable that what to include here and what not to include is also somewhat arbitrary and subjective. Generally, we include results related to machine learning and prediction, and in particular, the increasing volume of improved analyses using Bayesian inference, while extremely important, is not generally included, unless there is an obvious data mining component. We also do not include descriptions of software packages, unless they have been used to give a particular science result. Omission of a particular result may or may not be deliberate. Suggestions for additions are welcome.General Science / VO-enabled science
Discovery of New ObjectsSeveral papers demonstrate the use of VO and data mining tools to discover new astronomical objects.
Improved processingMany techniques make improvements in the quality of processing or the time needed to achieve useful results, with often broad potential applicability to many different studies. This applicability is algorithmic, rather than simply the creation of new software (which is beyond the scope of this section).
Object Detection and ClassificationNumerous papers provide improved detections and classifications of objects. We present examples where there was a demonstrable, qualitative change in the quality of results compared to a 'traditional' method, rather than just minor improvement.
Photometric RedshiftsSimilar to classification, numerous papers provide results for photometric redshifts.
Data Mining LiteratureThese links provide an overview of the astronomy literature that uses the main methods of data mining. Although it is trivial to, e.g., query ADS for "neural networks", these queries add some simple refinements to give a better overview. Nevertheless, because we limit the number of variations to keep control of the multiplicity of possible links, the returned results are not guaranteed complete. The numbers are the number of results returned by the query, as of Sep 26th 2011.
-- NickBall - 19 Mar 2011 -- NickBall - 23 Sep 2011 -- NickBall - 03 Oct 2011 |
IVOA KDD-IG: A user guide for Data Mining in Astronomy2: Examples of improved results enabled by data mining techniquesWe describe some examples where data mining techniques allowed improved astronomical results. The list aims to be comprehensive and illustrative of the range of results, but not exhaustive, or a literature review. The emphasis is on refereed articles, or published conference proceedings. We begin with science results, including Virtual-Observatory-enabled science and the discovery of new objects, then improved utility results such as processing, object detection and classification, and photometric redshifts. The emphasis is on results enabled by KDD and astroinfomatics, although inevitably there is overlap with astrostatistics, computer science, and other fields. Broadly, the section can be summarized as a collection of examples where specific, significant improvement came from the methods, or improved access to existing data, rather than new data. However, if a result is from a combination of both new data and astroinformatics methods (e.g., the data might otherwise be intractable), then such results are also included. Papers are given in approximate chronological order, newest first. Because our aim here is not to review everything, but rather to select highlights, we provide links to online databases, e.g., the NASA Astrophysics Data system, which give a more complete overview of the full range of astronomy literature in which data mining was used. Given the rather nebulous definition of data mining, it is inevitable that what to include here and what not to include is also somewhat arbitrary and subjective. Generally, we include results related to machine learning and prediction, and in particular, the increasing volume of improved analyses using Bayesian inference, while extremely important, is not generally included, unless there is an obvious data mining component. We also do not include descriptions of software packages, unless they have been used to give a particular science result. Omission of a particular result may or may not be deliberate. Suggestions for additions are welcome.General Science / VO-enabled science
Discovery of New ObjectsSeveral papers demonstrate the use of VO and data mining tools to discover new astronomical objects. | ||||||||||||||||||||||||||||||||||
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Improved processingMany techniques make improvements in the quality of processing or the time needed to achieve useful results, with often broad potential applicability to many different studies. This applicability is algorithmic, rather than simply the creation of new software (which is beyond the scope of this section).
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Object Detection and ClassificationNumerous papers provide improved detections and classifications of objects. We present examples where there was a demonstrable, qualitative change in the quality of results compared to a 'traditional' method, rather than just minor improvement.
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Photometric RedshiftsSimilar to classification, numerous papers provide results for photometric redshifts.
Data Mining LiteratureThese links provide an overview of the astronomy literature that uses the main methods of data mining. Although it is trivial to, e.g., query ADS for "neural networks", these queries add some simple refinements to give a better overview. Nevertheless, because we limit the number of variations to keep control of the multiplicity of possible links, the returned results are not guaranteed complete. The numbers are the number of results returned by the query, as of Sep 26th 2011.
-- NickBall - 19 Mar 2011 -- NickBall - 23 Sep 2011 -- NickBall - 03 Oct 2011 <--
|
IVOA KDD-IG: A user guide for Data Mining in Astronomy2: Examples of improved results enabled by data mining techniquesWe describe some examples where data mining techniques allowed improved astronomical results. The list aims to be comprehensive and illustrative of the range of results, but not exhaustive, or a literature review. The emphasis is on refereed articles, or published conference proceedings. We begin with science results, including Virtual-Observatory-enabled science and the discovery of new objects, then improved utility results such as processing, object detection and classification, and photometric redshifts. The emphasis is on results enabled by KDD and astroinfomatics, although inevitably there is overlap with astrostatistics, computer science, and other fields. | ||||||||||||||||||||||||||||||||||
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< < | Broadly, the section can be summarized as a collection of examples where specific, significant improvement came from the methods, or improved access to existing data, rather than new data. However, if a result is from a combination of both new data and astroinformatics methods (e.g., the data might otherwise be intractable), then such results are also included. Papers are given in approximate chronological order, newest first. | |||||||||||||||||||||||||||||||||
> > | Broadly, the section can be summarized as a collection of examples where specific, significant improvement came from the methods, or improved access to existing data, rather than new data. However, if a result is from a combination of both new data and astroinformatics methods (e.g., the data might otherwise be intractable), then such results are also included. Papers are given in approximate chronological order, newest first. | |||||||||||||||||||||||||||||||||
Because our aim here is not to review everything, but rather to select highlights, we provide links to online databases, e.g., the NASA Astrophysics Data system, which give a more complete overview of the full range of astronomy literature in which data mining was used. | ||||||||||||||||||||||||||||||||||
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< < | Given the rather nebulous definition of data mining, it is inevitable that what to include here and what not to include is also somewhat nebulous (and subjective). Generally, we include results related to machine learning and prediction, and in particular, the increasing volume of improved analyses using Bayesian inference, while extremely important, is not generally included, unless there is an obvious data mining component. We also do not include descriptions of software packages, unless they have been used to give a particular science result. | |||||||||||||||||||||||||||||||||
> > | Given the rather nebulous definition of data mining, it is inevitable that what to include here and what not to include is also somewhat arbitrary and subjective. Generally, we include results related to machine learning and prediction, and in particular, the increasing volume of improved analyses using Bayesian inference, while extremely important, is not generally included, unless there is an obvious data mining component. We also do not include descriptions of software packages, unless they have been used to give a particular science result. | |||||||||||||||||||||||||||||||||
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< < | Omission of a particular result may or may not be deliberate. Suggestions for additions are always welcome. | |||||||||||||||||||||||||||||||||
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Discovery of New ObjectsSeveral papers demonstrate the use of VO and data mining tools to discover new astronomical objects. | ||||||||||||||||||||||||||||||||||
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Improved processingMany techniques make improvements in the quality of processing or the time needed to achieve useful results, with often broad potential applicability to many different studies. This applicability is algorithmic, rather than simply the creation of new software (which is beyond the scope of this section). | ||||||||||||||||||||||||||||||||||
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Object Detection and ClassificationNumerous papers provide improved detections and classifications of objects. We present examples where there was a demonstrable, qualitative change in the quality of results compared to a 'traditional' method, rather than just minor improvement. | ||||||||||||||||||||||||||||||||||
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Data Mining LiteratureThese links provide an overview of the astronomy literature that uses the main methods of data mining. Although it is trivial to, e.g., query ADS for "neural networks", these queries add some simple refinements to give a better overview. Nevertheless, because we limit the number of variations to keep control of the multiplicity of possible links, the returned results are not guaranteed complete. The numbers are the number of results returned by the query, as of Sep 26th 2011.
-- NickBall - 19 Mar 2011 -- NickBall - 23 Sep 2011 | ||||||||||||||||||||||||||||||||||
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< < | -- NickBall - 26 Sep 2011 | |||||||||||||||||||||||||||||||||
> > | -- NickBall - 03 Oct 2011 | |||||||||||||||||||||||||||||||||
<--
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IVOA KDD-IG: A user guide for Data Mining in Astronomy2: Examples of improved results enabled by data mining techniquesWe describe some examples where data mining techniques allowed improved astronomical results. The list aims to be comprehensive and illustrative of the range of results, but not exhaustive, or a literature review. The emphasis is on refereed articles, or published conference proceedings. We begin with science results, including Virtual-Observatory-enabled science and the discovery of new objects, then improved utility results such as processing, object detection and classification, and photometric redshifts. The emphasis is on results enabled by KDD and astroinfomatics, although inevitably there is overlap with astrostatistics, computer science, and other fields. Broadly, the section can be summarized as a collection of examples where specific, significant improvement came from the methods, or improved access to existing data, rather than new data. However, if a result is from a combination of both new data and astroinformatics methods (e.g., the data might otherwise be intractable), then such results are also included. Papers are given in approximate chronological order, newest first. Because our aim here is not to review everything, but rather to select highlights, we provide links to online databases, e.g., the NASA Astrophysics Data system, which give a more complete overview of the full range of astronomy literature in which data mining was used. Given the rather nebulous definition of data mining, it is inevitable that what to include here and what not to include is also somewhat nebulous (and subjective). Generally, we include results related to machine learning and prediction, and in particular, the increasing volume of improved analyses using Bayesian inference, while extremely important, is not generally included, unless there is an obvious data mining component. We also do not include descriptions of software packages, unless they have been used to give a particular science result. Omission of a particular result may or may not be deliberate. Suggestions for additions are always welcome.General Science / VO-enabled science
Discovery of New ObjectsSeveral papers demonstrate the use of VO and data mining tools to discover new astronomical objects.
Improved processingMany techniques make improvements in the quality of processing or the time needed to achieve useful results, with often broad potential applicability to many different studies. This applicability is algorithmic, rather than simply the creation of new software (which is beyond the scope of this section).
Object Detection and ClassificationNumerous papers provide improved detections and classifications of objects. We present examples where there was a demonstrable, qualitative change in the quality of results compared to a 'traditional' method, rather than just minor improvement.
Photometric RedshiftsSimilar to classification, numerous papers provide results for photometric redshifts.
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< < | These links provide an overview of the astronomy literature that uses the main methods of data mining. Although it is trivial to query ADS, these queries add some refinement that gives a better overview. Nevertheless, because we limit the number of variations to keep control of the multiplicity of possible links, some papers may be missed. | ||||||||||||||||||||||||||||||
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IVOA KDD-IG: A user guide for Data Mining in Astronomy2: Examples of improved results enabled by data mining techniques | ||||||||
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< < | We describe some examples where data mining techniques allowed improved astronomical results. The list aims to be comprehensive and illustrative of the range of results, but not exhaustive, or a literature review. | |||||||
> > | We describe some examples where data mining techniques allowed improved astronomical results. The list aims to be comprehensive and illustrative of the range of results, but not exhaustive, or a literature review. The emphasis is on refereed articles, or published conference proceedings. | |||||||
We begin with science results, including Virtual-Observatory-enabled science and the discovery of new objects, then improved utility results such as processing, object detection and classification, and photometric redshifts. The emphasis is on results enabled by KDD and astroinfomatics, although inevitably there is overlap with astrostatistics, computer science, and other fields. | ||||||||
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< < | Broadly, the section can be summarized as a collection of examples where specific, significant improvement came from the methods, or improved access to existing data, rather than new data. However, if a result is from a combination of both new data and astroinformatics methods (e.g., the data might otherwise be intractable), then such results are also included. | |||||||
> > | Broadly, the section can be summarized as a collection of examples where specific, significant improvement came from the methods, or improved access to existing data, rather than new data. However, if a result is from a combination of both new data and astroinformatics methods (e.g., the data might otherwise be intractable), then such results are also included. Papers are given in approximate chronological order, newest first. | |||||||
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> > | Because our aim here is not to review everything, but rather to select highlights, we provide links to online databases, e.g., the NASA Astrophysics Data system, which give a more complete overview of the full range of astronomy literature in which data mining was used. | |||||||
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> > | Given the rather nebulous definition of data mining, it is inevitable that what to include here and what not to include is also somewhat nebulous (and subjective). Generally, we include results related to machine learning and prediction, and in particular, the increasing volume of improved analyses using Bayesian inference, while extremely important, is not generally included, unless there is an obvious data mining component. We also do not include descriptions of software packages, unless they have been used to give a particular science result. | |||||||
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Discovery of New ObjectsSeveral papers demonstrate the use of VO and data mining tools to discover new astronomical objects. | ||||||||
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Improved processingMany techniques make improvements in the quality of processing or the time needed to achieve useful results, with often broad potential applicability to many different studies. This applicability is algorithmic, rather than simply the creation of new software (which is beyond the scope of this section). | ||||||||
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Object Detection and ClassificationNumerous papers provide improved detections and classifications of objects. We present examples where there was a demonstrable, qualitative change in the quality of results compared to a 'traditional' method, rather than just minor improvement. | ||||||||
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Photometric RedshiftsSimilar to classification, numerous papers provide results for photometric redshifts. | ||||||||
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> > | Data Mining LiteratureThese links provide an overview of the astronomy literature that uses the main methods of data mining. Although it is trivial to query ADS, these queries add some refinement that gives a better overview. Nevertheless, because we limit the number of variations to keep control of the multiplicity of possible links, some papers may be missed. [To do] | |||||||
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<--
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IVOA KDD-IG: A user guide for Data Mining in Astronomy2: Examples of improved results enabled by data mining techniquesWe describe some examples where data mining techniques allowed improved astronomical results. The list aims to be comprehensive and illustrative of the range of results, but not exhaustive, or a literature review. | ||||||||
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< < | We begin with science results, including VO-enabled science and the discovery of new objects, then improved utility results such as processing, object detection, classification, and photometric redshifts. The emphasis is on results enabled by KDD and astroinfomatics, although inevitably there is overlap with astrostatistics, computer science, and other fields. | |||||||
> > | We begin with science results, including Virtual-Observatory-enabled science and the discovery of new objects, then improved utility results such as processing, object detection and classification, and photometric redshifts. The emphasis is on results enabled by KDD and astroinfomatics, although inevitably there is overlap with astrostatistics, computer science, and other fields. | |||||||
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< < | Broadly, the section can be summarized as a collection of examples where significant improvement came from the methods, or improved access to existing data, rather than new data. However, if a result is from a combination of both new data and astroinformatics methods (e.g., the data might otherwise be intractable), then such results are also included. | |||||||
> > | Broadly, the section can be summarized as a collection of examples where specific, significant improvement came from the methods, or improved access to existing data, rather than new data. However, if a result is from a combination of both new data and astroinformatics methods (e.g., the data might otherwise be intractable), then such results are also included. | |||||||
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Discovery of New ObjectsSeveral papers demonstrate the use of VO and data mining tools to discover new astronomical objects. | ||||||||
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Improved processingMany techniques make improvements in the quality of processing or the time needed to achieve useful results, with often broad potential applicability to many different studies. This applicability is algorithmic, rather than simply the creation of new software (which is beyond the scope of this section). | ||||||||
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Photometric RedshiftsSimilar to classification, numerous papers provide results for photometric redshifts. | ||||||||
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<--
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IVOA KDD-IG: A user guide for Data Mining in Astronomy2: Examples of improved results enabled by data mining techniquesWe describe some examples where data mining techniques allowed improved astronomical results. The list aims to be comprehensive and illustrative of the range of results, but not exhaustive, or a literature review. We begin with science results, including VO-enabled science and the discovery of new objects, then improved utility results such as processing, object detection, classification, and photometric redshifts. The emphasis is on results enabled by KDD and astroinfomatics, although inevitably there is overlap with astrostatistics, computer science, and other fields. Broadly, the section can be summarized as a collection of examples where significant improvement came from the methods, or improved access to existing data, rather than new data. However, if a result is from a combination of both new data and astroinformatics methods (e.g., the data might otherwise be intractable), then such results are also included. Papers are given in approximate chronological order, newest first.General Science / VO-enabled science
Discovery of New ObjectsSeveral papers demonstrate the use of VO and data mining tools to discover new astronomical objects.
Improved processingMany techniques make improvements in the quality of processing or the time needed to achieve useful results, with often broad potential applicability to many different studies. This applicability is algorithmic, rather than simply the creation of new software (which is beyond the scope of this section).
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Object Detection and ClassificationNumerous papers provide improved detections and classifications of objects. We present examples where there was a demonstrable, qualitative change in the quality of results compared to a 'traditional' method, rather than just incremental improvement.
Photometric RedshiftsSimilar to classification, numerous papers provide results for photometric redshifts.
-- NickBall - 18 Mar 2011 <--
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IVOA KDD-IG: A user guide for Data Mining in Astronomy | ||||||||
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< < | 2: Examples of improved science enabled by data mining techniques | |||||||
> > | 2: Examples of improved results enabled by data mining techniques | |||||||
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< < | We describe some examples where data mining techniques allowed improved science results. | |||||||
> > | We describe some examples where data mining techniques allowed improved astronomical results. The list aims to be comprehensive and illustrative of the range of results, but not exhaustive, or a literature review. | |||||||
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< < | ||||||||
> > | We begin with science results, including VO-enabled science and the discovery of new objects, then improved utility results such as processing, object detection, classification, and photometric redshifts. The emphasis is on results enabled by KDD and astroinfomatics, although inevitably there is overlap with astrostatistics, computer science, and other fields. | |||||||
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< < | KDD-IG members: please add examples here! | |||||||
> > | Broadly, the section can be summarized as a collection of examples where significant improvement came from the methods, or improved access to existing data, rather than new data. However, if a result is from a combination of both new data and astroinformatics methods (e.g., the data might otherwise be intractable), then such results are also included. | |||||||
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> > | Papers are given in approximate chronological order, newest first.
General Science / VO-enabled science
Discovery of New ObjectsSeveral papers demonstrate the use of VO and data mining tools to discover new astronomical objects.
Improved processingMany techniques make improvements in the quality of processing or the time needed to achieve useful results, with often broad potential applicability to many different studies. This applicability is algorithmic, rather than simply the creation of new software (which is beyond the scope of this section).
Object Detection and ClassificationNumerous papers provide improved detections and classifications of objects. We present examples where there was a demonstrable, qualitative change in the quality of results compared to a 'traditional' method, rather than just incremental improvement.
Photometric RedshiftsSimilar to classification, numerous papers provide results for photometric redshifts.
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<--
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IVOA KDD-IG: A user guide for Data Mining in Astronomy2: Examples of improved science enabled by data mining techniquesWe describe some examples where data mining techniques allowed improved science results.KDD-IG members: please add examples here! -- NickBall - 05 Sep 2010 <--
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