Improved pattern extraction scheme for clustering multidimensional data

Multidimensional data refers to data that contains at least three attributes or dimensions. The availability of huge amount of multidimensional data that has been collected over the years has greatly challenged the ability to digest the data and to gain useful knowledge that would otherwise be lost. Clustering technique has enabled the manipulation of this knowledge to gain an interesting pattern analysis that could benefit the relevant parties. In this study, three crucial challenges in extracting the pattern of the multidimensional data are highlighted: the dimension of huge multidimensional data requires efficient exploration method for the pattern extraction, the need for better mechanisms to test and validate clustering results and the need for more informative visualization to interpret the “best” clusters. Densitybased clustering algorithms such as density-based spatial clustering application with noise (DBSCAN), density clustering (DENCLUE) and kernel fuzzy C-means (KFCM) that use probabilistic similarity function have been introduced by previous works to determine the number of clusters automatically. However, they have difficulties in dealing with clusters of different densities, shapes and size. In addition, they require many parameter inputs that are difficult to determine. Kernel-nearestneighbor (KNN)-density-based clustering including kernel-nearest-neighbor-based clustering (KNNClust) has been proposed to solve the problems of determining smoothing parameters for multidimensional data and to discover cluster with arbitrary shape and densities. However, KNNClust faces problem on clustering data with different size. Therefore, this research proposed a new pattern extraction scheme integrating triangular kernel function and local average density technique called TKC to improve KNN-density-based clustering algorithm. The improved scheme has been validated experimentally with two scenarios: using real multidimensional spatio-temporal data and using various classification datasets. Four different measurements were used to validate the clustering results; Dunn and Silhouette index to assess the quality, F-measure to evaluate the performance of approach in terms of accuracy, ANOVA test to analyze the cluster distribution, and processing time to measure the efficiency. The proposed scheme was benchmarked with other well-known clustering methods including KNNClust, Iterative Local Gaussian Clustering (ILGC), basic k-means, KFCM, DBSCAN and DENCLUE. The results on the classification dataset demonstrated that TKC produced clusters with higher accuracy and more efficient than other clustering methods. In addition, the analysis of the results showed that the proposed TKC scheme is capable of handling multidimensional data, validated by Silhouette and Dunn index which was close to one, indicating reliable results

Modeling geometric-temporal context with directional pyramid co-occurrence for action recognition

In this paper, we present a new geometric-temporal representation for visual action recognition based on local spatio-temporal features. First, we propose a modified covariance descriptor under the log-Euclidean Riemannian metric to represent the spatio-temporal cuboids detected in the video sequences. Compared with previously proposed covariance descriptors, our descriptor can be measured and clustered in Euclidian space. Second, to capture the geometric-temporal contextual information, we construct a directional pyramid co-occurrence matrix (DPCM) to describe the spatio-temporal distribution of the vector-quantized local feature descriptors extracted from a video. DPCM characterizes the co-occurrence statistics of local features as well as the spatio-temporal positional relationships among the concurrent features. These statistics provide strong descriptive power for action recognition. To use DPCM for action recognition, we propose a directional pyramid co-occurrence matching kernel to measure the similarity of videos. The proposed method achieves the state-of-the-art performance and improves on the recognition performance of the bag-of-visual-words (BOVWs) models by a large margin on six public data sets. For example, on the KTH data set, it achieves 98.78% accuracy while the BOVW approach only achieves 88.06%. On both Weizmann and UCF CIL data sets, the highest possible accuracy of 100% is achieved

Penerapan Triangular Kernel Nearest Neighbor Sebagai Metode Clustering Dasar Pada Metode Bagging

Salah satu permasalahan utama dalam data mining adalah untuk menemukan metode clustering yang powerful. Terdapat suatu pendekatan baru dengan menggabungkan lebih dari satu metode clustering dikenal juga sebagai multi-clustering. Salah satu metode yang telah diperkenalkan adalah bagging (bootstrap aggregating). Metode tersebut terdiri dari metode clustering dasar dan metode clustering hierarki untuk mengkombinasikan partisi yang dihasilkan oleh metode clustering dasar. Triangular kernel nearest neighbor (TKNN) merupakan salah satu metode clustering berbasis densitas yang memiliki kemampuan untuk menghasilkan jumlah cluster secara otomatis. Penelitian ini menggunakan TKNN sebagai metode clustering dasar dalam metode bagging. Analisis komparatif menggunakan nilai F-Measures untuk empat metode single clustering (TKNN, ILGC, DBSCAN dan DENCLUE) dengan 9 dataset untuk menganalisa kinerja dari metode yang diusulkan. Berdasarkan hasil percobaan, menunjukkan beberapa hasil cluster yang lebih baik

Efficient Human Activity Recognition in Large Image and Video Databases

Vision-based human action recognition has attracted considerable interest in recent research for its applications to video surveillance, content-based search, healthcare, and interactive games. Most existing research deals with building informative feature descriptors, designing efficient and robust algorithms, proposing versatile and challenging datasets, and fusing multiple modalities. Often, these approaches build on certain conventions such as the use of motion cues to determine video descriptors, application of off-the-shelf classifiers, and single-factor classification of videos. In this thesis, we deal with important but overlooked issues such as efficiency, simplicity, and scalability of human activity recognition in different application scenarios: controlled video environment (e.g.~indoor surveillance), unconstrained videos (e.g.~YouTube), depth or skeletal data (e.g.~captured by Kinect), and person images (e.g.~Flicker). In particular, we are interested in answering questions like (a) is it possible to efficiently recognize human actions in controlled videos without temporal cues? (b) given that the large-scale unconstrained video data are often of high dimension low sample size (HDLSS) nature, how to efficiently recognize human actions in such data? (c) considering the rich 3D motion information available from depth or motion capture sensors, is it possible to recognize both the actions and the actors using only the motion dynamics of underlying activities? and (d) can motion information from monocular videos be used for automatically determining saliency regions for recognizing actions in still images

Multi-Variate Time Series Similarity Measures and Their Robustness Against Temporal Asynchrony

abstract: The amount of time series data generated is increasing due to the integration of sensor technologies with everyday applications, such as gesture recognition, energy optimization, health care, video surveillance. The use of multiple sensors simultaneously for capturing different aspects of the real world attributes has also led to an increase in dimensionality from uni-variate to multi-variate time series. This has facilitated richer data representation but also has necessitated algorithms determining similarity between two multi-variate time series for search and analysis. Various algorithms have been extended from uni-variate to multi-variate case, such as multi-variate versions of Euclidean distance, edit distance, dynamic time warping. However, it has not been studied how these algorithms account for asynchronous in time series. Human gestures, for example, exhibit asynchrony in their patterns as different subjects perform the same gesture with varying movements in their patterns at different speeds. In this thesis, we propose several algorithms (some of which also leverage metadata describing the relationships among the variates). In particular, we present several techniques that leverage the contextual relationships among the variates when measuring multi-variate time series similarities. Based on the way correlation is leveraged, various weighing mechanisms have been proposed that determine the importance of a dimension for discriminating between the time series as giving the same weight to each dimension can led to misclassification. We next study the robustness of the considered techniques against different temporal asynchronies, including shifts and stretching. Exhaustive experiments were carried on datasets with multiple types and amounts of temporal asynchronies. It has been observed that accuracy of algorithms that rely on data to discover variate relationships can be low under the presence of temporal asynchrony, whereas in case of algorithms that rely on external metadata, robustness against asynchronous distortions tends to be stronger. Specifically, algorithms using external metadata have better classification accuracy and cluster separation than existing state-of-the-art work, such as EROS, PCA, and naive dynamic time warping.Dissertation/ThesisMasters Thesis Computer Science 201