An Algorithm for Mining Gradual Moving Object Clusters Pattern From Trajectory Streams

The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams.This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models.By processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters pattern.Firstly,the density peaks clustering algorithm is exploited to identify clusters of different snapshots.The stable relationship between relatively few moving objects is used to improve the clustering efficiency.Then,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is updated.The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process.Finally,experiment results on two real datasets demonstrate that our algorithm is effective and efficient.

Research on Chinese Industrial Cluster Patterns ——Take Haier Household Electrical Appliance as an Example

Industrial cluster is an aggregation of the related enterprises and institutions in certain field and in certain place. There are mainly three patterns for industrial clusters： market-oriented cluster, foreign fund oriented cluster and endogenous brand oriented cluster. Many economists have established their own theories about the patterns industrial cluster, but there are few dealing with one big enterprises and its cluster pattern. This paper, taking Haier household electrical appliance as an example, is trying to study one model of Chinese industrial cluster and analyze its characteristics with their effects on economy, as.well as the advantages together with the related suggestions from the authors.

What is the relationship between ecosystem services and urbanization? A case study of the mountainous areas in Southwest China

China is undergoing a rapid urbanization process,and urbanization will have a direct impact on regional ecosystems and affect regional ecosystem services.Considering the mountainous counties in Southwestern China as the research object,this study reveals the spatial clustering characteristics of four typical ecosystem services(food production,soil conservation,water yield and carbon sequestration)as well as the trade-offs and synergies among ecosystem services in different urbanized areas.At the same time,piecewise linear regression is used to determine the threshold of the influence of urbanization on ecosystem services.The results indicate that:1)There are spatial autocorrelations among the four typical ecosystem services;with strong clustering characteristics,the positive correlation types are"clustered"locally;and with significant spatial heterogeneity,the negative correlation types are scattered and mainly appear in the highly urbanized area.2)There are also remarkable differences in the relationship among various ecosystem services in different urbanized areas,and in particular,there are marked trade-offs between food production and carbon sequestration in the moderately urbanized area and the highly urbanized area.However,there are synergies between them in the lowly urbanized area.3)With an increase in the compounded night light index(CNLI),water yield,carbon sequestration,food production and overall ecosystem services values present an increasing-decreasing trend,the soil conservation function value shows a decreasing-increasing trend.The response of water yield,carbon sequestration,food production,and overall ecosystem services to the compounded night light index(CNLI)has a threshold of 1.2642,1.4833,1.3388,1.5146 and 1.2237,respectively.Based on the detected relationships between urbanization and ecosystem services,this study provides a theoretical reference for the selection of urbanization development models in key ecological functional areas.

Application of digital image processing to the determination of spatial distribution of earthquakes

This paper has proposed a practical method for determining the spatial distribution pattern of earthquakes by means of digital image processing and has given some calculation results. This method can overcome the artificial arbitrariness which is usually inevitable in determining the spatial distribution of earthquakes. Meanwhile, the form of seismic gaps and the method for identifying seismic gaps have also been discussed. It should be pointed out that the method proposed in this paper is a new idea in this respect. However, this method is still unable to determine the seismic gap uniquely. In order to identify the real seismic gap, comprehensive analysis of the variation of other relevant parameters (e. g., the b-value, etc. ) should also be made. The results are as follows; Thephenomenon that an area of high seismic activity is surrounded by low seismicity areas, i. e., the seismic clustering pattern is the general form of spatial distribution of earthquakes; whereas a low seismicity area surrounded by high seismicity areas, i. e., the seismic gap pattern, is only an important form of spatial distribution of earthquakes. Earthquakes often occur in a certain seismic clustering area or on its margin.

Symptom clustering in chronic gastritis based on spectral clustering

OBJECTIVE: Apply spectral clustering to analyze the patterns of symptoms in patients with chronic gastritis(CG).METHODS: Based on 919 CG subjects, we applied mutual information feature selection to choose the positively correlated symptoms with each pattern.Then, we used the Shi and Malik spectral clustering algorithm to select the top 20 correlated symptoms.RESULTS: We ascertained the results of six patterns.There were three categories for the pattern of accumulation of damp heat in the spleen-stomach(0.00332). There were six categories for the pattern of dampness obstructing the spleen-stomach(0.02466). There were two categories for the pattern of spleen-stomach Qi deficiency(0.013 89).There were three categories for the pattern of spleen-stomach deficiency cold(0.009 15). There were five categories for the pattern of liver-Qistagnation(0.01910).There were four categories for the pattern of stagnant heat in the liver-stomach(0.00585).CONCLUSION: Most of the spectral clustering results of the symptoms of CG patterns were in accordance with clinical experience and Traditional Chinese Medicine theory. Most categories suggested the nature and/or location of the disease.

Online Induction of Probabilistic Real-Time Automata

The probabilistic real-time automaton （PRTA） is a representation of dynamic processes arising in the sciences and industry. Currently, the induction of automata is divided into two steps： the creation of the prefix tree acceptor （PTA） and the merge procedure based on clustering of the states. These two steps can be very time intensive when a PRTA is to be induced for massive or even unbounded datasets. The latter one can be efficiently processed, as there exist scalable online clustering algorithms. However, the creation of the PTA still can be very time consuming. To overcome this problem, we propose a genuine online PRTA induction approach that incorporates new instances by first collapsing them and then using a maximum frequent pattern based clustering. The approach is tested against a predefined synthetic automaton and real world datasets, for which the approach is scalable and stable. Moreover, we present a broad evaluation on a real world disease group dataset that shows the applicability of such a model to the analysis of medical processes.