XML Retrieval with Results Clustering on Android

2013 ◽  
Vol 756-759 ◽  
pp. 1300-1303
Author(s):  
Peng Fei Liu ◽  
Yan Hua Chen ◽  
Wen Jie Xie ◽  
Qiao Yi Hu
Keyword(s):  
Information Management ◽  
Mobile Devices ◽  
Clustering Algorithm ◽  
Data Sets ◽  
Mobile Platforms ◽  
Xml Retrieval ◽  
Design And Implementation ◽  
Clustering Model ◽  
Computing Platforms ◽  
Retrieval Engine

XML receives widely interests in data exchanging and information management on both traditional desktop computing platforms and rising mobile computing platforms. However, traditional XML retrieval does not work on mobile devices due to the mobile platforms limitations and diversities. Considering that XML retrieval on mobile devices will become increasingly popular, in this article, we have paid attention to the design and implementation of XML retrieval and results clustering model on the android platform, building on jaxen and dom4j, the XML parser and retrieval engine; furthermore, the K-means clustering algorithm. As an example of usage, we have tested the prototype on some data sets to the mobile scenario and illustrated the feasibility of the proposed approach. The model demonstrated in this article is available on the mobile XML Retrieval project website: http://code.google.com/p/mobilexmlretrieval/.

The Gamut of E-Government Research, Design, and Implementation

2015 ◽  
pp. 1-20
Author(s):  
Kelvin Joseph Bwalya ◽  
Stephen M. Mutula
Keyword(s):  
Information Management ◽  
Mobile Devices ◽  
Research Design ◽  
Design Principles ◽  
Research And Practice ◽  
Design And Implementation ◽  
Government Project ◽  
Government Research ◽  
Contemporary Technology ◽  
Key Questions

E-Government research and practice has changed over the years to incorporate recent and contemporary technology developments and unique in evolving contextual environments. Further, the emerging conceptualization of service and applications interactions is slowly defining the gamut of e-Government research and practice. On another front, there has been a dynamic transition of e-Government being implemented on Web3.0 from the original Web2.0 platforms and advanced e-Government applications accessible on mobile devices i.e. ubiquitous or mobile government. Web3.0 presents a semantic platform allowing responsive man-machine interfaces and applications integration facilitating advanced information management possibilities. The chapter explores the contemporary issues in e-Government and articulates the pertinent factors that need to be interrogated for successful and sustainable e-Government development. Key questions of e-Government and the design principles that need to be taken into consideration in any e-Government project are explored.

The fast clustering algorithm for the big data based on K-means

2020 ◽  
Vol 18 (06) ◽  
pp. 2050053
Author(s):  
Ting Xie ◽  
Taiping Zhang
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Feature Space ◽  
Data Sets ◽  
Benchmark Data ◽  
Clustering Model ◽  
Alternating Direction ◽  
Learning Technique ◽  
Noise Data

As a powerful unsupervised learning technique, clustering is the fundamental task of big data analysis. However, many traditional clustering algorithms for big data that is a collection of high dimension, sparse and noise data do not perform well both in terms of computational efficiency and clustering accuracy. To alleviate these problems, this paper presents Feature K-means clustering model on the feature space of big data and introduces its fast algorithm based on Alternating Direction Multiplier Method (ADMM). We show the equivalence of the Feature K-means model in the original space and the feature space and prove the convergence of its iterative algorithm. Computationally, we compare the Feature K-means with Spherical K-means and Kernel K-means on several benchmark data sets, including artificial data and four face databases. Experiments show that the proposed approach is comparable to the state-of-the-art algorithm in big data clustering.

The Gamut of E-Government Research, Design, and Implementation

2015 ◽  
pp. 49-68
Author(s):  
Kelvin Joseph Bwalya ◽  
Stephen M. Mutula
Keyword(s):  
Information Management ◽  
Mobile Devices ◽  
Research Design ◽  
Design Principles ◽  
Research And Practice ◽  
Design And Implementation ◽  
Government Project ◽  
Government Research ◽  
Contemporary Technology ◽  
Key Questions

E-Government research and practice has changed over the years to incorporate recent and contemporary technology developments and unique in evolving contextual environments. Further, the emerging conceptualization of service and applications interactions is slowly defining the gamut of e-Government research and practice. On another front, there has been a dynamic transition of e-Government being implemented on Web3.0 from the original Web2.0 platforms and advanced e-Government applications accessible on mobile devices i.e. ubiquitous or mobile government. Web3.0 presents a semantic platform allowing responsive man-machine interfaces and applications integration facilitating advanced information management possibilities. The chapter explores the contemporary issues in e-Government and articulates the pertinent factors that need to be interrogated for successful and sustainable e-Government development. Key questions of e-Government and the design principles that need to be taken into consideration in any e-Government project are explored.

Self-Tuning Possibilistic c-Means Clustering Models

2019 ◽  
Vol 27 (Supp01) ◽  
pp. 143-159
Author(s):  
László Szilágyi ◽  
Szidónia Lefkovits ◽  
Sándor M. Szilágyi
Keyword(s):  
Clustering Algorithm ◽  
Standard Test ◽  
Data Sets ◽  
Robust Algorithms ◽  
Fuzzy C Means ◽  
Second Stage ◽  
Clustering Model ◽  
Valid Cluster ◽  
Fuzzy C Means Clustering ◽  
Self Tuning

The relaxation of the probabilistic constraint of the fuzzy c-means clustering model was proposed to provide robust algorithms that are insensitive to strong noise and outlier data. These goals were achieved by the possibilistic c-means (PCM) algorithm, but these advantages came together with a sensitivity to cluster prototype initialization. According to the original recommendations, the probabilistic fuzzy c-means (FCM) algorithm should be applied to establish the cluster initialization and possibilistic penalty terms for PCM. However, when FCM fails to provide valid cluster prototypes due to the presence of noise, PCM has no chance to recover and produce a fine partition. This paper proposes a two-stage c-means clustering algorithm to tackle with most problems enumerated above. In the first stage called initialization, FCM with two modifications is performed: (1) extra cluster added for noisy data; (2) extra variable and constraint added to handle clusters of various diameters. In the second stage, a modified PCM algorithm is carried out, which also contains the cluster width tuning mechanism based on which it adaptively updates the possibilistic penalty terms. The proposed algorithm has less parameters than PCM when the number of clusters is [Formula: see text]. Numerical evaluation involving synthetic and standard test data sets proved the advantages of the proposed clustering model.

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

2020 ◽  
Vol 13 (5) ◽  
pp. 641-649
Author(s):  
Yuancheng Li ◽  
Yaqi Cui ◽  
Xiaolong Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Residential Areas ◽  
Processing Power ◽  
Learning Machine ◽  
Advanced Metering ◽  
Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

scholarly journals Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

2021 ◽  
Vol 13 (9) ◽  
pp. 4648
Author(s):  
Rana Muhammad Adnan ◽  
Kulwinder Singh Parmar ◽  
Salim Heddam ◽  
Shamsuddin Shahid ◽  
Ozgur Kisi
Keyword(s):  
Clustering Algorithm ◽  
Suspended Sediments ◽  
Ecological Impacts ◽  
Accurate Estimation ◽  
Data Sets ◽  
The Novel ◽  
Neuro Fuzzy ◽  
Adaptive Regression ◽  
Novel Method ◽  
Model Training

The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50–50% for model training and testing in the first scenario, and the training and test data sets are swapped in the second scenario. In Guangyuan Station, the MARS–KM showed a performance improvement compared to ANFIS, MARS, and M5Tree methods in term of RMSE by 39%, 30%, and 18% in the first scenario and by 24%, 22%, and 8% in the second scenario, respectively, while the improvement in RMSE of ANFIS, MARS, and M5Tree was 34%, 26%, and 27% in the first scenario and 7%, 16%, and 6% in the second scenario, respectively, at Beibei Station. Additionally, the MARS–KM models provided much more satisfactory estimates using only discharge values as inputs.

The Player Load Associated With Typical Activities in Elite Netball

2017 ◽  
Vol 12 (9) ◽  
pp. 1218-1223 ◽  
Author(s):  
Jared A. Bailey ◽  
Paul B. Gastin ◽  
Luke Mackey ◽  
Dan B. Dwyer
Keyword(s):  
Training Programs ◽  
Data Sets ◽  
Specific Training ◽  
Start Time ◽  
Total Load ◽  
Future Design ◽  
Design And Implementation ◽  
Video Recordings ◽  
Wearable Accelerometers

Context:Most previous investigations of player load in netball have used subjective methodologies, with few using objective methodologies. While all studies report differences in player activities or total load between playing positions, it is unclear how the differences in player activity explain differences in positional load. Purpose:To objectively quantify the load associated with typical activities for all positions in elite netball. Methods:The player load of all playing positions in an elite netball team was measured during matches using wearable accelerometers. Video recordings of the matches were also analyzed to record the start time and duration of 13 commonly reported netball activities. The load associated with each activity was determined by time-aligning both data sets (load and activity). Results:Off-ball guarding produced the highest player load per instance, while jogging produced the greatest player load per match. Nonlocomotor activities contributed least to total match load for attacking positions (goal shooter [GS], goal attack [GA], and wing attack [WA]) and most for defending positions (goalkeeper [GK], goal defense [GD], and wing defense [WD]). Specifically, centers (Cs) produced the greatest jogging load, WA and WD accumulated the greatest running load, and GS and WA accumulated the greatest shuffling load. WD and Cs accumulated the greatest guarding load, while WD and GK accumulated the greatest off-ball guarding load. Conclusions:All positions exhibited different contributions from locomotor and nonlocomotor activities toward total match load. In addition, the same activity can have different contributions toward total match load, depending on the position. This has implications for future design and implementation of position-specific training programs.

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