The BANG-clustering system: Grid-based data analysis

Author(s):  
Erich Schikuta ◽  
Martin Erhart
Keyword(s):  
2008 ◽  
Vol 9 (1) ◽  
pp. 493 ◽  
Author(s):  
G Barton ◽  
J Abbott ◽  
N Chiba ◽  
DW Huang ◽  
Y Huang ◽  
...  

2020 ◽  
Vol 8 (5) ◽  
pp. 4044-4049

Clustering is one of the most significant ideas in data mining. It is an unsupervised learning model. Clustering technique in handling high dimensional data is more complex due to intrinsic sparsity nature of high dimensional data. Though, existing methods to reduce immaterial clusters were based on spectral clustering algorithm and graph-based learning algorithm, whose lack of sparsity and polynomial time complexity compromises their efficiency when applied to sparse high dimensional data. This paper concentrates to cluster the sparsely distributed high dimensional data objects. Fuzzy Relational Scattered Distance Based Clustering (FRSDBC) method is developed with three models such as Geometric Median Based Fuzzy model, Scattered Distance measure model, Grid based clustered sparse data representation model. Geometric Median Based Fuzzy model calculates the geometric median of similar sparse data and then the non similar sparse data objects to fitting the relational fuzziness across data points. It involves in the subspace reduction of data objects. Scattered Distance measure model is used to measure the distance between the inner and outer object. Grid based clustering is used to calculate the area of the cluster in FRSDBC method. The main idea of the FRSDBC method is to clustering data points over sparsely distributed data within limited processing time. The Clustering Time, Clustering Accuracy and Space Complexity of each method is analyzed. The result of the FRSDBC method is compared with other techniques, the results obtained are more accurate, easy to understand and the clustering time was substantially low in FRSDBC method. It is widely used in many practical applications such as weather forecast, share trading, medical data analysis and aerial data analysis.


2021 ◽  
Vol 245 ◽  
pp. 01012
Author(s):  
Jun Li ◽  
WeiWei Miao ◽  
WenDong Zhang

The development of wind power and other new energy sources has a great impact on the stability of power system frequency. By analyzing the characteristics of the primary frequency control(PFC) assessment standard of the power grid, one real-time monitoring method of the unit’s PFC capability of the power grid based on data analysis is proposed. The Power network dispatching department can fully grasps the overall frequency regulation capability of the operating units. It can improve the grid’s response methods to deal with high power gaps and ensure the safe and stable operation of the grid.


Clusteringisaprocedureofdividing a lot of information (or objects) into a lot of significant sub-classes, called bunches, help clients comprehend the characteristic gathering or structure in an informational index. Clustering has wide applications, in Economic Science (particularly statistical surveying), Document order, Recognition, Spatial Data Analysis and Image Processing Thewaytowardgatheringalotofphysicalor dynamic items into classes of same articlesis called grouping. A group is an accumulation of information questions that are near each other inside a similar bunch and are not atall like the articles in different groups. A bunch of information articles can be dealt with together as one gathering thus might be considered as a type of informationpressure. In spite of the fact that characterization is a compelling methods for recognizing gatherings or classes of items, it requires the frequently exorbitant naming of a huge arrangement of preparing tuples or examples, which the classifier uses to display each gathering. Clustering is likewise called information division in certain applications since clustering parcels substantial informational collections into gatherings as per their similitude. A decent clustering strategy will deliver top notch groups intra- class (that is, intra-group) comparability is high, the between class likeness is low and nature of a clustering result additionally relies upon closeness measure utilized by the technique and ,the nature of a clustering technique is likewise estimated by its capacity to find a few or the majority of the concealed examples ,nonetheless, target assessment is dangerously normally done by human/master examination. When all is said in done, the significant grouping techniques can be isolated into the accompanying classifications Partitioning strategies, various leveled techniques, Density-based strategies, Grid-based strategies, Model- basedtechniques.Thispaperutilizesfuzzycimplies grouping with PSO for clustering procedure of diabetic forecastdataset.


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