Rough clustering for web transactions

Grouping web transactions into clusters is important in order to obtain better understanding of user's behavior. Currently, the rough approximation-based clustering technique has been used to group web transactions into clusters. It is based on the similarity of upper approximations of transactions by given threshold. However, the processing time is still an issue due to the high complexity for finding the similarity of upper approximations of a transaction which used to merge between two or more clusters. In this study, an alternative technique for grouping web transactions using rough set theory is proposed. It is based on the two similarity classes which is nonvoid intersection. The technique is implemented in MATLAB ® version 7.6.0.324 (R2008a). The two UCI benchmark datasets taken from: http:/kdd.ics.uci.edu/ databases/msnbc/msnbc.html and http:/kdd.ics.uci.edu/databases/ Microsoft / microsoft.html are opted in the simulation processes. The simulation reveals that the proposed technique significantly requires lower response time up to 62.69 % and 66.82 % as compared to the rough approximation-based clustering, severally. Meanwhile, for cluster purity it performs better until 2.5 % and 14.47%, respectively

Automatic differentiation based for particle swarm optimization Steepest descent direction

Particle swam optimization (PSO) is one of the most effective optimization methods to find the global optimum point. In other hand, the descent direction (DD) is the gradient based method that has the local search capability. The combination of both methods is promising and interesting to get the method with effective global search capability and efficient local search capability. However, In many application, it is difficult or impossible to obtain the gradient exactly of an objective function. In this paper, we propose Automatic differentiation (AD) based for PSODD. we compare our methods on benchmark function. The results shown that the combination methods give us a powerful tool to find the solution

PENERAPAN MODEL-MODEL PEMBELAJARAN DAN TEKNOLOGI DI ERA INDUSTRI 4.0 DI SEKOLAH DASAR

Era revolusi industri 4.0 mengalihkan peralatan yang bersifat tradisional menuju alat – alat yang serba digital. Penyuluhan mengenai model –model pembelajaran dan teknologi dalam pembelajaran dilakukan di SD Muhammadiyah Mertosanan yang  bertujuan untuk memberi wawasan kepada guru-guru SD Muhammadiyah Mertosanan mengenai berbagai macam model pembelajaran dan berbagai teknologi yang berkaitan dengan pembelajaran. Metode yang digunakan untuk mencapai tujuan tersebut adalah penyuluhan yang dilakukan secara on line. Hasil yang diperoleh dari pelaksanaan kegiatan ini adalah mendapat respon yang positip dari para peserta pengabdian dengan banyaknya pertanyaan-pertanyaan yang diajukan dan adanya masukan untuk diadakan kegiatan pengabdian lanjutan

Alternative Technique Reducing Complexity of Maximum Attribute Relation

Clustering refers to the method grouping the large data into the smaller groups based on the similarity measure. Clustering techniques have been applied on numerical, categorical and mix data. One of the categorical data clustering technique based on the soft set theory is Maximum Attribute Relation (MAR). The MAR technique allows calculating all of pair multi soft set made. However, the computational complexity is still an issue of the technique. To overcome the drawback, the paper proposes the alternative algorithm to decrease the complexity so get the faster response time. In this paper, to get the similar results as MAR without calculating all pair of soft set is proved. The alternative algorithm is implemented in MATLAB Software, and then experimental is run on the 10 benchmark datasets. The results show that the alternative algorithm improves the computational complexity in term of response time up to 36.46%

Automatic Differentiation Based for Particle Swarm Optimization Steepest Descent Direction

Particle swam optimization (PSO) is one of the most effective optimization methods to find the global optimum point. In other hand, the descent direction (DD) is the gradient based method that has the local search capability. The combination of both methods is promising and interesting to get the method with effective global search capability and efficient local search capability. However, In many application, it is difficult or impossible to obtain the gradient exactly of an objective function. In this paper, we propose Automatic differentiation (AD) based for PSODD. We compare our methods on benchmark function. The results shown that the combination methods give us a powerful tool to find the solution

A Framework of Clustering Based on Chicken Swarm Optimization

Chicken Swarm Optimization (CSO) algorithm which is one of the most recently introduced optimization algorithms, simulates the intelligent foraging behaviour of chicken swarm. Data clustering is used in many disciplines and applications. It is an important tool and a descriptive task seeking to identify homogeneous groups of objects based on the values of their attributes. In this work, CSO is used for data clustering. The performance of the proposed CSO was assessed on several data sets and compared with well known and recent metaheuristic algorithm for clustering: Particle Swarm Optimization (PSO) algorithm , Cuckoo Search (CS) and Bee Colony Algorithm (BC). The simulation results indicate that CSO algorithm have much potential and can efficiently be used for data clustering

A Comparative Analysis of Rough Sets for Incomplete Information System in Student Dataset

Rough set theory is a mathematical model for dealing with the vague, imprecise, and uncertain knowledge that has been successfully used to handle incomplete information system. Since we know that in fact, in the real-world problems, it is regular to find conditions where the user is not able to provide all the necessary preference values. In this paper, we compare the performance accuracy of the extension of rough set theory, i.e. Tolerance Relation, Limited Tolerance Relation, Non-Symmetric Similarity Relation and New Limited Tolerance Relation of Rough Sets for handling incomplete information system in real-world student dataset. Based on the results, it is shown that New Limited Tolerance Relation of Rough Sets has outperformed the previous techniques.

Soft set theory based decision support system for mining electronic government dataset

Electronic government (e-gov) is applied to support performance and create more efficient and effective public services. Grouping data in soft-set theory can be considered as a decision-making technique for determining the maturity level of e-government use. So far, the uncertainty of the data obtained through the questionnaire has not been maximally used as an appropriate reference for the government in determining the direction of future e-gov development policy. This study presents the maximum attribute relative (MAR) based on soft set theory to classify attribute options. The results show that facilitation conditions (FC) are the highest variable in influencing people to use e-government, followed by performance expectancy (PE) and system quality (SQ). The results provide useful information for decision makers to make policies about their citizens and potentially provide recommendations on how to design and develop e-government systems in improving public services

Histogram Thersholding for Automatic Color Segmentation Based on K-means Clustering

Abstract. Color segmentation method has been proposed and developed by many researchers, however it still become a challenging topic on how to automatically segment color image based on color information. This research proposes a method to estimate number of color and performs color segmentation. The method initiates cluster centers using histogram thresholding and peak selection on CIE L*a*b* chromatic channels. k-means is performed to find optimal cluster centers and to assign each color data into color labels using previously estimated clusters centers. Finally, initial color labels can be split or merge in order to segment black, dark, bright, or white color using luminosity histogram. The final cluster is evaluated using silhouette to measure the cluster quality and calculate the accuracy of color label prediction. The result shows that the proposed method achieves up to 85% accuracy on 20 test images and average silhouette value is 0.694 on 25 test images. Keywords: Automatic color segmentation; Histogram thresholding; Cluster centers initialization; k-means clustering