A novel fuzzy similarity measure and prevalence estimation approach for similarity profiled temporal association pattern mining

2018 ◽  
Vol 83 ◽  
pp. 582-595 ◽  
Author(s):  
Vangipuram Radhakrishna ◽  
Shadi A. Aljawarneh ◽  
P.V. Kumar ◽  
V. Janaki
Keyword(s):  
Similarity Measure ◽  
Pattern Mining ◽  
Temporal Association ◽  
Association Pattern ◽  
Prevalence Estimation ◽  
Fuzzy Similarity ◽  
Temporal Association Pattern

Spatial-Temporal Association of Duration of Peak Period at Signalized Intersections

2013 ◽  
Vol 330 ◽  
pp. 1008-1014
Author(s):  
Liang Hui Qian ◽  
Hua Zhang ◽  
Xiao Hong Chen
Keyword(s):  
Spatial Interaction ◽  
Relative Size ◽  
Interval Data ◽  
Signalized Intersections ◽  
Temporal Association ◽  
Time Interval ◽  
Peak Period ◽  
Association Pattern ◽  
Temporal Association Pattern ◽  
Peak Spreading

In the case of peak spreading, the traditional static analysis on the duration of congestion using peak 15 minutes of hour has lost its meaning. To deal with congestion, we need dynamic analysis on the onset and dissipation of congestion. This paper aims to reflect the severity of congestion, even the spatial interaction of congestion, by the relative size of peak period. Two thresholds, the minimum value in the peak hour and 95 percent of peak hour volume respectively, were chosen to define the peak using archived stop-bar detector data. The thresholds and the time-granularity of the data were cross-compared to choose appropriate threshold and data time interval, and the result is 95 percent of peak hour volume under 10min interval data. Then the measures of duration of peak period, including the length of peak period, the beginning and ending time of peak period, were calculated for different signalized intersections inlet approaches. Further, the measures of peak period of different intersections in the same direction of the same radial road were presented to find out the commute traffic patterns. Lastly, the spatial-temporal association pattern of the measures of peak period of different intersections in downtown Shanghai was performed by ArcGIS.

Handling Fuzzy Similarity for Data Classification

2011 ◽  
pp. 796-802 ◽  
Author(s):  
Roy Gelbard ◽  
Avichai Meged
Keyword(s):  
Data Mining ◽  
Similarity Measure ◽  
Data Representation ◽  
Classification Algorithms ◽  
Binary Representation ◽  
Fuzzy Data ◽  
Problem Domain ◽  
Data Types ◽  
Fuzzy Similarity ◽  
Set Up

Representing and consequently processing fuzzy data in standard and binary databases is problematic. The problem is further amplified in binary databases where continuous data is represented by means of discrete ‘1’ and ‘0’ bits. As regards classification, the problem becomes even more acute. In these cases, we may want to group objects based on some fuzzy attributes, but unfortunately, an appropriate fuzzy similarity measure is not always easy to find. The current paper proposes a novel model and measure for representing fuzzy data, which lends itself to both classification and data mining. Classification algorithms and data mining attempt to set up hypotheses regarding the assigning of different objects to groups and classes on the basis of the similarity/distance between them (Estivill-Castro & Yang, 2004) (Lim, Loh & Shih, 2000) (Zhang & Srihari, 2004). Classification algorithms and data mining are widely used in numerous fields including: social sciences, where observations and questionnaires are used in learning mechanisms of social behavior; marketing, for segmentation and customer profiling; finance, for fraud detection; computer science, for image processing and expert systems applications; medicine, for diagnostics; and many other fields. Classification algorithms and data mining methodologies are based on a procedure that calculates a similarity matrix based on similarity index between objects and on a grouping technique. Researches proved that a similarity measure based upon binary data representation yields better results than regular similarity indexes (Erlich, Gelbard & Spiegler, 2002) (Gelbard, Goldman & Spiegler, 2007). However, binary representation is currently limited to nominal discrete attributes suitable for attributes such as: gender, marital status, etc., (Zhang & Srihari, 2003). This makes the binary approach for data representation unattractive for widespread data types. The current research describes a novel approach to binary representation, referred to as Fuzzy Binary Representation. This new approach is suitable for all data types - nominal, ordinal and as continuous. We propose that there is meaning not only to the actual explicit attribute value, but also to its implicit similarity to other possible attribute values. These similarities can either be determined by a problem domain expert or automatically by analyzing fuzzy functions that represent the problem domain. The added new fuzzy similarity yields improved classification and data mining results. More generally, Fuzzy Binary Representation and related similarity measures exemplify that a refined and carefully designed handling of data, including eliciting of domain expertise regarding similarity, may add both value and knowledge to existing databases.

scholarly journals M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 707 ◽  
Author(s):  
Tran Manh Tuan ◽  
Luong Thi Hong Lan ◽  
Shuo-Yan Chou ◽  
Tran Thi Ngan ◽  
Le Hoang Son ◽  
...  
Keyword(s):  
Decision Making ◽  
Similarity Measure ◽  
Fuzzy Inference System ◽  
Granular Computing ◽  
Fuzzy Inference ◽  
Fuzzy Theory ◽  
Rule Base ◽  
Inference System ◽  
Fuzzy Measures ◽  
Fuzzy Similarity

Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS.

scholarly journals UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model

2019 ◽  
Vol 8 (11) ◽  
pp. 518 ◽  
Author(s):  
Ning Guo ◽  
Shashi Shekhar ◽  
Wei Xiong ◽  
Luo Chen ◽  
Ning Jing
Keyword(s):  
Similarity Measure ◽  
Pattern Mining ◽  
Clustering Methods ◽  
Trajectory Data ◽  
Real World Data ◽  
Asynchronous Sampling ◽  
Ellipse Model ◽  
Spatiotemporal Clustering ◽  
Motion Features ◽  
Trajectory Similarity

Measuring the similarity between a pair of trajectories is the basis of many spatiotemporal clustering methods and has wide applications in trajectory pattern mining. However, most measures of trajectory similarity in the literature are based on precise models that ignore the inherent uncertainty in trajectory data recorded by sensors. Traditional computing or mining approaches that assume the preciseness and exactness of trajectories therefore risk underperforming or returning incorrect results. To address the problem, we propose an amended ellipse model which takes both interpolation error and positioning error into account by making use of motion features of trajectory to compute the ellipse’s shape parameters. A specialized similarity measure method considering uncertainty called UTSM based on the model is also proposed. We validate the approach experimentally on both synthetic and real-world data and show that UTSM is not only more robust to noise and outliers but also more tolerant of different sample frequencies and asynchronous sampling of trajectories.

Spectral clustering with fuzzy similarity measure

2011 ◽  
Vol 21 (6) ◽  
pp. 701-709 ◽  
Author(s):  
Feng Zhao ◽  
Hanqiang Liu ◽  
Licheng Jiao
Keyword(s):  
Similarity Measure ◽  
Spectral Clustering ◽  
Fuzzy Similarity
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