Spatial equilibrium of housing provident fund in China based on data mining cluster analysis

2016 ◽  
Vol 10 (2) ◽  
pp. 138
Author(s):  
Huafu Jiang ◽  
Guangbin Wang
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Spatial Equilibrium ◽  
Provident Fund ◽  
Housing Provident Fund

A Data Distribution View of Clustering Algorithms

2011 ◽  
pp. 374-381 ◽  
Author(s):  
Junjie Wu ◽  
Jian Chen ◽  
Hui Xiong
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Data Distribution ◽  
Point Of View ◽  
Group Method ◽  
Data Sets ◽  
Distribution Point

Cluster analysis (Jain & Dubes, 1988) provides insight into the data by dividing the objects into groups (clusters), such that objects in a cluster are more similar to each other than objects in other clusters. Cluster analysis has long played an important role in a wide variety of fields, such as psychology, bioinformatics, pattern recognition, information retrieval, machine learning, and data mining. Many clustering algorithms, such as K-means and Unweighted Pair Group Method with Arithmetic Mean (UPGMA), have been wellestablished. A recent research focus on clustering analysis is to understand the strength and weakness of various clustering algorithms with respect to data factors. Indeed, people have identified some data characteristics that may strongly affect clustering analysis including high dimensionality and sparseness, the large size, noise, types of attributes and data sets, and scales of attributes (Tan, Steinbach, & Kumar, 2005). However, further investigation is expected to reveal whether and how the data distributions can have the impact on the performance of clustering algorithms. Along this line, we study clustering algorithms by answering three questions: 1. What are the systematic differences between the distributions of the resultant clusters by different clustering algorithms? 2. How can the distribution of the “true” cluster sizes make impact on the performances of clustering algorithms? 3. How to choose an appropriate clustering algorithm in practice? The answers to these questions can guide us for the better understanding and the use of clustering methods. This is noteworthy, since 1) in theory, people seldom realized that there are strong relationships between the clustering algorithms and the cluster size distributions, and 2) in practice, how to choose an appropriate clustering algorithm is still a challenging task, especially after an algorithm boom in data mining area. This chapter thus tries to fill this void initially. To this end, we carefully select two widely used categories of clustering algorithms, i.e., K-means and Agglomerative Hierarchical Clustering (AHC), as the representative algorithms for illustration. In the chapter, we first show that K-means tends to generate the clusters with a relatively uniform distribution on the cluster sizes. Then we demonstrate that UPGMA, one of the robust AHC methods, acts in an opposite way to K-means; that is, UPGMA tends to generate the clusters with high variation on the cluster sizes. Indeed, the experimental results indicate that the variations of the resultant cluster sizes by K-means and UPGMA, measured by the Coefficient of Variation (CV), are in the specific intervals, say [0.3, 1.0] and [1.0, 2.5] respectively. Finally, we put together K-means and UPGMA for a further comparison, and propose some rules for the better choice of the clustering schemes from the data distribution point of view.

scholarly journals METHOD OF PREDICTION OF THERMAL CONDUCTIVITY COEFFICIENT OF WALL MATERIALS CONTAINING SALTS / SIENŲ DRUSKINGŲ MEDŽIAGŲ ŠILUMOS LAIDUMO KOEFICIENTO PROGNOZĖS METODAS

2011 ◽  
Vol 17 (1) ◽  
pp. 108-114
Author(s):  
Marta Kosior-Kazberuk ◽  
Valeriy Ezerskiy
Keyword(s):  
Thermal Conductivity ◽  
Data Mining ◽  
Cluster Analysis ◽  
Porous Structure ◽  
Thermal Conductivity Coefficient ◽  
Wall Material ◽  
Corrective Factor ◽  
Real Value ◽  
Service Conditions ◽  
Wall Materials

The salt presence in porous structure of wall materials causes changes in thermal conductivity. The real value of material thermal conductivity in service conditions is necessary for engineering applications. The method of prediction of the thermal conductivity coefficient for wall materials containing salt using corrective factor is presented in the paper. By means of corrective coefficients, for well-known content of moisture and salt in wall material, it is possible to calculate thermal conductivity coefficient with regard to the presence of specific salt or mix of salts in material. The corrective coefficient values were determined for different groups of salts. The partition of salts into groups was made by means of cluster analysis, in dependence on their influence on material thermal conductivity. Clustering, in data mining, is a useful tool for discovering groups and identifying interesting distributions in the underlying data. Santrauka Druskos poringųjų medžiagų sienų struktūroje sukelia jų šilumos laidumo kitimą. Tikroji eksploatacinė medžiagų šilumos laidumo koeficiento vertė būtina atliekant inžinerinius tyrimus. Straipsnyje pristatomas sienų, kurių medžiagos turi druskų, šilumos laidumo koeficiento prognozės metodas naudojant pataisos daugiklį. Naudojant pataisos koeficientus, esant tiksliai žinomam drėgmės ir druskų kiekiui sienos medžiagoje, galima apskaičiuoti šilumos laidumo koeficientą, atsižvelgus į tam tikrų druskų ar jų mišinių buvimą medžiagoje. Buvo nustatytos įvairių druskų grupių pataisos koeficientų vertės. Druskos buvo paskirstytos grupėmis pagal klasterinę analizę, priklausomai nuo jų įtakos medžiagos šilumos laidumui. Klasterių metodas yra naudinga priemonė duomenims apdoroti—grupėms atskleisti ir įdomiems duomenų pasiskirstymams rasti.

Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty

2010 ◽  
Vol 37 (7) ◽  
pp. 5259-5264 ◽  
Author(s):  
Seyed Mohammad Seyed Hosseini ◽  
Anahita Maleki ◽  
Mohammad Reza Gholamian
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Customer Loyalty ◽  
Data Mining Approach ◽  
Using Data
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