Mining Spatial-Temporal Patterns and Structural Sparsity for Human Motion Data Denoising
نویسندگان
چکیده
منابع مشابه
Mining Spatial and Spatio-temporal Patterns in Scientific Data
This paper focusses on designing and applying data mining techniques to analyze spatial and spatiotemporal data originated in scientific domains. Data mining is the process of discovering hidden and meaningful knowledge in a data set. It has been successfully applied to many real-life problems, for instance, web personalization, network intrusion detection, and customized Marketing. This paper ...
متن کاملUnderstanding Temporal Human Mobility Patterns in a City by Mobile Cellular Data Mining, Case Study: Tehran City
Recent studies have shown that urban complex behaviors like human mobility should be examined by newer and smarter methods. The ubiquitous use of mobile phones and other smart communication devices helps us use a bigger amount of data that can be browsed by the hours of the day, the days of the week, geographic area, meteorological conditions, and so on. In this article, mobile cellular data mi...
متن کاملFuzzy Structural Primitives for Spatial Data Mining
− Spatial data mining knows a more and more important interest. Fundamental processes of spatial data mining are in particular clustering and structural patterns detection. These processes are influenced strongly by the concept of proximity or neighborhood. This paper introduces some structures to the construction of a spatial data mining integrating fuzzy structural primitives and propose to o...
متن کاملT-Patterns Revisited: Mining for Temporal Patterns in Sensor Data
The trend to use large amounts of simple sensors as opposed to a few complex sensors to monitor places and systems creates a need for temporal pattern mining algorithms to work on such data. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. We contrast several recent approaches to the problem, and extend the T-Pattern algorit...
متن کاملDeviation and Association Patterns for Subgroup Mining in Temporal, Spatial, and Textual Data Bases
The paradox of the heap of grains in respect to roughness, fuzziness, and negligibility p. 19 Rough sets-what are they about? p. 24 Reasoning about data-a rough set perspective p. 25 Information granulation and its centrality in human and machine intelligence p. 35 Classification strategies using certain and possible rules p. 37 Well-behaviored operations for approximate sets p. 45 Searching fo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Cybernetics
سال: 2015
ISSN: 2168-2267,2168-2275
DOI: 10.1109/tcyb.2014.2381659