Implementasi Kombinasi Algoritme Self-Organizing Map dan Fuzzy C-Means untuk Pengelompokan Performa Belajar Siswa pada Media Pembelajaran Digital

Media pembelajaran digital mampu menyimpan data dalam bentuk log data yang dapat digunakan untuk melihat perbedaan performa siswa yang tentu saja berbeda-beda antara satu siswa dengan siswa yang lainnya. Perbedaan performa siswa tersebut menyebabkan dibutuhkannya sebuah tahapan yang berfungsi untuk mempermudah proses evaluasi dengan cara menempatkan siswa kedalam kelompok yang sesuai agar dapat membantu tenaga pengajar dalam menangani serta memberikan umpan balik yang tepat pada siswanya. Penelitian ini bertujuan memanfaatkan log data dari sebuah media pembelajaran digital dengan menggunakan kombinasi dari algoritme Self-Organizing Map dan Fuzzy C-Means untuk mengelompokan siswa berdasarkan aktivitas mereka selama belajar dengan media tersebut. Data akan melalui sebuah proses reduksi dimensi dengan menggunakan algoritme SOM, lalu dikelompokkan dengan menggunakan algoritme FCM. Selanjutnya, data dievaluasi dengan menggunakan nilai silhouette coefficient dan dibandingkan dengan algoritme SOM clustering konvensional. Berdasarkan hasil implementasi yang telah dilakukan menggunakan 12 data assignment pada media pembelajaran Monsakun, dihasilkan parameter-parameter optimal seperti ukuran map atau jumlah output neuron sejumlah 25x25 dengan nilai learning rate yang berbeda-beda disetiap assignment. Selain itu, diperoleh pula 2 kelompok siswa pada setiap assignment berdasarkan nilai silhouette coefficient tertinggi yang mencapai lebih dari 0.8 di beberapa assignment. Melalui serangkaian pengujian yang telah dilakukan, penerapan kombinasi algoritme SOM dan FCM secara signifikan menghasilkan cluster yang lebih baik dibandingkan dengan algoritme SOM clustering konvensional. Abstract Digital learning media is able to store data in the form of log data that can be used to see differences in student performance. The difference in student performance causes the need for a stage that functions to simplify the evaluation process by placing students into appropriate groups in order to assist the teaching staff in handling and providing appropriate feedback to students. This study aims to utilize log data from a digital learning media using a combination of the Self-Organizing Map algorithm and Fuzzy C-Means to classify students based on their activities while learning with these media. The data will go through a dimensional reduction process using the SOM algorithm, then grouped using the FCM algorithm. Furthermore, the data were evaluated using the silhouette coefficient value and compared with the conventional SOM clustering algorithm. Based on the results of the implementation that has been carried out using 12 data assignments on the Monsakun learning media, optimal parameters such as map size or the number of neuron outputs are 25x25 with different learning rate values in each assignment. In addition, 2 groups of students were obtained for each assignment based on the highest silhouette coefficient score which reached more than 0.8 in several assignments. Through a series of tests that have been carried out, the implementation of a combination of the SOM and FCM algorithms has significantly better clusters than the conventional SOM clustering algorithm

Neural Collaborative Subspace Clustering

We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of low-dimensional subspaces. In contrast to previous attempts, our model runs without the aid of spectral clustering. This makes our algorithm one of the kinds that can gracefully scale to large datasets. At its heart, our neural model benefits from a classifier which determines whether a pair of points lies on the same subspace or not. Essential to our model is the construction of two affinity matrices, one from the classifier and the other from a notion of subspace self-expressiveness, to supervise training in a collaborative scheme. We thoroughly assess and contrast the performance of our model against various state-of-the-art clustering algorithms including deep subspace-based ones.Comment: Accepted to ICML 201

Sketch-based subspace clustering of hyperspectral images

Sparse subspace clustering (SSC) techniques provide the state-of-the-art in clustering of hyperspectral images (HSIs). However, their computational complexity hinders their applicability to large-scale HSIs. In this paper, we propose a large-scale SSC-based method, which can effectively process large HSIs while also achieving improved clustering accuracy compared to the current SSC methods. We build our approach based on an emerging concept of sketched subspace clustering, which was to our knowledge not explored at all in hyperspectral imaging yet. Moreover, there are only scarce results on any large-scale SSC approaches for HSI. We show that a direct application of sketched SSC does not provide a satisfactory performance on HSIs but it does provide an excellent basis for an effective and elegant method that we build by extending this approach with a spatial prior and deriving the corresponding solver. In particular, a random matrix constructed by the Johnson-Lindenstrauss transform is first used to sketch the self-representation dictionary as a compact dictionary, which significantly reduces the number of sparse coefficients to be solved, thereby reducing the overall complexity. In order to alleviate the effect of noise and within-class spectral variations of HSIs, we employ a total variation constraint on the coefficient matrix, which accounts for the spatial dependencies among the neighbouring pixels. We derive an efficient solver for the resulting optimization problem, and we theoretically prove its convergence property under mild conditions. The experimental results on real HSIs show a notable improvement in comparison with the traditional SSC-based methods and the state-of-the-art methods for clustering of large-scale images