Data stream mining
The data stream mining problem has been studied extensively in recent years, due to the greatease in collection of stream data. The essential to a data stream mining algorithms is that we can only read data once. Unfortunately, most of traditional data mining algorithms do not have such single-scan...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2009
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| Online Access: | http://hdl.handle.net/10356/17010 |
| _version_ | 1811689032181088256 |
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| author | Wan, Li |
| author2 | Ng Wee Keong |
| author_facet | Ng Wee Keong Wan, Li |
| author_sort | Wan, Li |
| collection | NTU |
| description | The data stream mining problem has been studied extensively in recent years, due to the greatease in collection of stream data. The essential to a data stream mining algorithms is that we can only read data once. Unfortunately, most of traditional data mining algorithms do not have such single-scan property. Usually, data stream is considered as semi-in¯nite. It is impossible
to store all the past data with limited resources. Thus, mining high dimensional data streams
is a challenging task. In this report, we are going to propose some interesting observations on
feature quality stream(FQS), which is obtained from data stream in real time, and a frame-
work to analyze such stream. The analysis results of FQS are used to reduce the dimension of
data streams. We will also propose a data stream mining framework called MR-Stream. It is
a e±cient data stream clustering framework with the following properties: (1) computes and
updates synopsis information in constant time; (2) allows users to discover clusters at multiple
resolutions; (3) determines the right time for users to generate clusters from the synopsis in-
formation; (4) generates clusters of higher purity than existing algorithms; and (5) determines
the right threshold function for density-based clustering based on the fading model of stream
data. MR-Stream can be extend to solve classi¯cation problem. The classi¯cation results ob-
tained from the online component of MR-Stream framework are in realtime. The result given
by MR-Stream is presented as a probability distribution table over di®erent classes. |
| first_indexed | 2024-10-01T05:41:39Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/17010 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:41:39Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | ntu-10356/170102023-03-03T20:52:14Z Data stream mining Wan, Li Ng Wee Keong School of Computer Engineering Centre for Advanced Information Systems DRNTU::Engineering::Computer science and engineering::Information systems::Database management The data stream mining problem has been studied extensively in recent years, due to the greatease in collection of stream data. The essential to a data stream mining algorithms is that we can only read data once. Unfortunately, most of traditional data mining algorithms do not have such single-scan property. Usually, data stream is considered as semi-in¯nite. It is impossible to store all the past data with limited resources. Thus, mining high dimensional data streams is a challenging task. In this report, we are going to propose some interesting observations on feature quality stream(FQS), which is obtained from data stream in real time, and a frame- work to analyze such stream. The analysis results of FQS are used to reduce the dimension of data streams. We will also propose a data stream mining framework called MR-Stream. It is a e±cient data stream clustering framework with the following properties: (1) computes and updates synopsis information in constant time; (2) allows users to discover clusters at multiple resolutions; (3) determines the right time for users to generate clusters from the synopsis in- formation; (4) generates clusters of higher purity than existing algorithms; and (5) determines the right threshold function for density-based clustering based on the fading model of stream data. MR-Stream can be extend to solve classi¯cation problem. The classi¯cation results ob- tained from the online component of MR-Stream framework are in realtime. The result given by MR-Stream is presented as a probability distribution table over di®erent classes. Bachelor of Engineering (Computer Engineering) 2009-05-29T03:45:00Z 2009-05-29T03:45:00Z 2009 2009 Final Year Project (FYP) http://hdl.handle.net/10356/17010 en Nanyang Technological University 59 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Information systems::Database management Wan, Li Data stream mining |
| title | Data stream mining |
| title_full | Data stream mining |
| title_fullStr | Data stream mining |
| title_full_unstemmed | Data stream mining |
| title_short | Data stream mining |
| title_sort | data stream mining |
| topic | DRNTU::Engineering::Computer science and engineering::Information systems::Database management |
| url | http://hdl.handle.net/10356/17010 |
| work_keys_str_mv | AT wanli datastreammining |