Beyond BPMN Data Objects – Method Tailoring and Assessment

2015 ◽  
pp. 89-99
Author(s):  
Bartosz Marcinkowski ◽  
Bartlomiej Gawin
Keyword(s):  
Data Objects ◽  
Method Tailoring

Data Field for Hierarchical Clustering

2011 ◽  
Vol 7 (4) ◽  
pp. 43-63 ◽  
Author(s):  
Shuliang Wang ◽  
Wenyan Gan ◽  
Deyi Li ◽  
Deren Li
Keyword(s):  
Hierarchical Clustering ◽  
Physical Space ◽  
Core Data ◽  
Data Object ◽  
Group Data ◽  
Data Field ◽  
Data Objects ◽  
The Masses ◽  
Space Data ◽  
The Impact

In this paper, data field is proposed to group data objects via simulating their mutual interactions and opposite movements for hierarchical clustering. Enlightened by the field in physical space, data field to simulate nuclear field is presented to illuminate the interaction between objects in data space. In the data field, the self-organized process of equipotential lines on many data objects discovers their hierarchical clustering-characteristics. During the clustering process, a random sample is first generated to optimize the impact factor. The masses of data objects are then estimated to select core data object with nonzero masses. Taking the core data objects as the initial clusters, the clusters are iteratively merged hierarchy by hierarchy with good performance. The results of a case study show that the data field is capable of hierarchical clustering on objects varying size, shape or granularity without user-specified parameters, as well as considering the object features inside the clusters and removing the outliers from noisy data. The comparisons illustrate that the data field clustering performs better than K-means, BIRCH, CURE, and CHAMELEON.

scholarly journals Maintainability Evaluation of Object-Oriented Software System Using Clustering Techniques

2013 ◽  
Vol 5 (2) ◽  
pp. 136-143 ◽  
Author(s):  
Astha Mehra ◽  
Sanjay Kumar Dubey
Keyword(s):  
Clustering Algorithms ◽  
Daily Basis ◽  
Computer Assisted ◽  
Program Execution ◽  
Huge Data ◽  
Large Databases ◽  
Input Dataset ◽  
Data Objects

In today’s world data is produced every day at a phenomenal rate and we are required to store this ever growing data on almost daily basis. Even though our ability to store this huge data has grown but the problem lies when users expect sophisticated information from this data. This can be achieved by uncovering the hidden information from the raw data, which is the purpose of data mining.  Data mining or knowledge discovery is the computer-assisted process of digging through and analyzing enormous set of data and then extracting the meaning out of it. The raw and unlabeled data present in large databases can be classified initially in an unsupervised manner by making use of cluster analysis. Clustering analysis is the process of finding the groups of objects such that the objects in a group will be similar to one another and dissimilar from the objects in other groups. These groups are known as clusters.  In other words, clustering is the process of organizing the data objects in groups whose members have some similarity among them. Some of the applications of clustering are in marketing -finding group of customers with similar behavior, biology- classification of plants and animals given their features, data analysis, and earthquake study -observe earthquake epicenter to identify dangerous zones, WWW -document classification, etc. The results or outcome and efficiency of clustering process is generally identified though various clustering algorithms. The aim of this research paper is to compare two important clustering algorithms namely centroid based K-means and X-means. The performance of the algorithms is evaluated in different program execution on the same input dataset. The performance of these algorithms is analyzed and compared on the basis of quality of clustering outputs, number of iterations and cut-off factors.

scholarly journals PRIVACY PRESERVING CLUSTERING BASED ON LINEAR APPROXIMATION OF FUNCTION

2013 ◽  
Vol 12 (5) ◽  
pp. 3443-3451
Author(s):  
Rajesh Pasupuleti ◽  
Narsimha Gugulothu
Keyword(s):  
Linear Approximation ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Privacy Preserving ◽  
Distance Measures ◽  
Clustering Methods ◽  
Sensitive Data ◽  
Processing Information ◽  
Data Objects ◽  
Approximation Of Function

Clustering analysis initiatives  a new direction in data mining that has major impact in various domains including machine learning, pattern recognition, image processing, information retrieval and bioinformatics. Current clustering techniques address some of the  requirements not adequately and failed in standardizing clustering algorithms to support for all real applications. Many clustering methods mostly depend on user specified parametric methods and initial seeds of clusters are randomly selected by  user.  In this paper, we proposed new clustering method based on linear approximation of function by getting over all idea of behavior knowledge of clustering function, then pick the initial seeds of clusters as the points on linear approximation line and perform clustering operations, unlike grouping data objects into clusters by using distance measures, similarity measures and statistical distributions in traditional clustering methods. We have shown experimental results as clusters based on linear approximation yields good  results in practice with an example of  business data are provided.  It also  explains privacy preserving clusters of sensitive data objects.

Wavelet-Based Immune Fuzzy C-Means Algorithm

2014 ◽  
Vol 685 ◽  
pp. 638-641
Author(s):  
Zhi Xin Ma ◽  
Bin Bin Wen ◽  
Da Gan Nie
Keyword(s):  
Wavelet Transform ◽  
Fuzzy Clustering ◽  
Spatial Data ◽  
Reproductive Mode ◽  
Descent Direction ◽  
Fuzzy C Means ◽  
Objective Function Values ◽  
Data Objects ◽  
Fuzzy C Means Algorithm ◽  
Convergence Of Algorithm

Fuzzy clustering can express the ambiguity ofsample category, and better reflect the actual needs of datamining. By introducing wavelet transform and artificial immunealgorithm to fuzzy clustering, Wavelet-based Immune Fuzzy C-means Algorithm (WIFCM) is proposed for overcoming theimperfections of fuzzy clustering, such as falling easily into localoptimal solution, slower convergence speed and initialization-dependence of clustering centers. Innovations of WIFCM arethe elite extraction operator and the descent reproductive mode.Using the locality and multi-resolution of wavelet transform, theelite extraction operator explores the distribution and densityinformation of spatial data objects in multi-dimensional spaceto guide the search of cluster centers. Taking advantage ofthe relationship between the relative positions of elite centersand inferior centers, the descent reproductive mode obtains theapproximate fastest descent direction of objective function values,and assures fast convergence of algorithm. Compared to theclassic fuzzy C-means algorithm, experiments on 3 UCI data setsshow that WIFCM has obvious advantages in average numberof iterations and accuracy.

How to Build Functional Data Objects

2009 ◽  
pp. 45-58 ◽  
Author(s):  
J.O Ramsay ◽  
Giles Hooker ◽  
Spencer Graves
Keyword(s):  
Functional Data ◽  
Data Objects

Dynamic Allocation of Data-Objects in the Web, Using Self-tuning Genetic Algorithms

2004 ◽  
pp. 376-384
Author(s):  
Joaquín Pérez O. ◽  
Rodolfo A. Pazos R. ◽  
Graciela Mora O. ◽  
Guadalupe Castilla V. ◽  
José A. Martínez ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Dynamic Allocation ◽  
Self Tuning ◽  
Data Objects ◽  
The Web
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