4 research outputs found

    ΠšΠ»Π°ΡΡ‚Π΅Ρ€ΠΈΠ·Π°Ρ†ΠΈΡ ΠΌΠ½ΠΎΠ³ΠΎΠΌΠ΅Ρ€Π½Ρ‹Ρ… ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΎΠ² Ρ€Π°Π·Π»ΠΈΡ‡Π½ΠΎΠΉ ΠΏΡ€ΠΈΡ€ΠΎΠ΄Ρ‹: постановка ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΡΠΊΠΎΠΉ Π·Π°Π΄Π°Ρ‡ΠΈ

    Get PDF
    О кластСризации ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΎΠ² Ρ€Π°Π·Π»ΠΈΡ‡Π½ΠΎΠΉ ΠΏΡ€ΠΈΡ€ΠΎΠ΄Ρ‹, рассмотрСны Ρ‚Ρ€ΠΈ Π²ΠΈΠ΄Π° ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΎΠ²: скалярныС, Π²Π΅ΠΊΡ‚ΠΎΡ€Π½Ρ‹Π΅ ΠΈ ΠΌΠ°Ρ‚Ρ€ΠΈΡ‡Π½Ρ‹Π΅. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½ Π°Π½Π°Π»ΠΈΠ· запросов этих Ρ‚Π΅Ρ€ΠΌΠΈΠ½ΠΎΠ² Π½Π° русском ΠΈ английском языках Π² Google Schola

    Online Spectral Clustering on Network Streams

    Get PDF
    Graph is an extremely useful representation of a wide variety of practical systems in data analysis. Recently, with the fast accumulation of stream data from various type of networks, significant research interests have arisen on spectral clustering for network streams (or evolving networks). Compared with the general spectral clustering problem, the data analysis of this new type of problems may have additional requirements, such as short processing time, scalability in distributed computing environments, and temporal variation tracking. However, to design a spectral clustering method to satisfy these requirements certainly presents non-trivial efforts. There are three major challenges for the new algorithm design. The first challenge is online clustering computation. Most of the existing spectral methods on evolving networks are off-line methods, using standard eigensystem solvers such as the Lanczos method. It needs to recompute solutions from scratch at each time point. The second challenge is the parallelization of algorithms. To parallelize such algorithms is non-trivial since standard eigen solvers are iterative algorithms and the number of iterations can not be predetermined. The third challenge is the very limited existing work. In addition, there exists multiple limitations in the existing method, such as computational inefficiency on large similarity changes, the lack of sound theoretical basis, and the lack of effective way to handle accumulated approximate errors and large data variations over time. In this thesis, we proposed a new online spectral graph clustering approach with a family of three novel spectrum approximation algorithms. Our algorithms incrementally update the eigenpairs in an online manner to improve the computational performance. Our approaches outperformed the existing method in computational efficiency and scalability while retaining competitive or even better clustering accuracy. We derived our spectrum approximation techniques GEPT and EEPT through formal theoretical analysis. The well established matrix perturbation theory forms a solid theoretic foundation for our online clustering method. We facilitated our clustering method with a new metric to track accumulated approximation errors and measure the short-term temporal variation. The metric not only provides a balance between computational efficiency and clustering accuracy, but also offers a useful tool to adapt the online algorithm to the condition of unexpected drastic noise. In addition, we discussed our preliminary work on approximate graph mining with evolutionary process, non-stationary Bayesian Network structure learning from non-stationary time series data, and Bayesian Network structure learning with text priors imposed by non-parametric hierarchical topic modeling

    Π‘ΠΎΠ²Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Π΅ ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹ ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎ-экономичСских систСм Π² условиях Π³Π»ΠΎΠ±Π°Π»ΠΈΠ·Π°Ρ†ΠΈΠΈ

    Get PDF
    Π’ сборникС прСдставлСны Ρ€Π°Π±ΠΎΡ‚Ρ‹ российских ΠΈ Π·Π°Ρ€ΡƒΠ±Π΅ΠΆΠ½Ρ‹Ρ… ΡƒΡ‡Π΅Π½Ρ‹Ρ…, посвящСнныС Π°ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹ΠΌ ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΠ°ΠΌ развития ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎ-экономичСских систСм Π² условиях Π³Π»ΠΎΠ±Π°Π»ΠΈΠ·Π°Ρ†ΠΈ
    corecore