17699590
Adapting K-Means Algorithm for Discovering Clusters in Subspaces
Subspace clustering is a challenging task in the field of data mining. Traditional distance measures fail to differentiate the furthest point from the nearest point in very high dimensional data space. To tackle the problem, we design minimal subspace distance which measures the similarity between two points in the subspace where they are nearest to each other. It can discover subspace clusters implicitly when measuring the similarities between points. We use the new similarity measure to improve traditional k-means algorithm for discovering clusters in subspaces. By clustering with low-dimensional minimal subspace distance first, the clusters in low-dimensional subspaces are detected. Then by gradually increasing the dimension of minimal subspace distance, the clusters get refined in higher dimensional subspaces. Our experiments on both synthetic data and real data show the effectiveness of the proposed similarity measure and algorithm.
28734493
AGRID: An Efficient Algorithm for Clustering Large High-Dimensional Datasets
The clustering algorithm GDILC relies on density-based clustering with grid and is designed to discover clusters of arbitrary shapes and eliminate noises. However, it is not scalable to large high-dimensional datasets. In this paper, we improved this algorithm in five important directions. Through these improvements, AGRID is of high scalability and can process large high-dimensional datasets. It can discover clusters of various shapes and eliminate noises effectively. Besides, it is insensitive to the order of input and is a non-parametric algorithm. The high speed and accuracy of the AGRID clustering algorithm was shown in our experiments.
38902380
Construction Algorithm of Principal Curves in the Sense of Limit
Principal curves have been defined as self-consistent, smooth, one-dimensional curves which pass through the middle of a multidimensional data set. They are nonlinear generalization of the first Principal Component. In this paper, we take a new approach by defining principal curves as continuous curves based on the local tangent space in the sense of limit. It is proved that this new principal curves not only satisfy the self-consistent property, but also are the unique existence for any given open covering. Based on the new definition, a new practical algorithm for constructing principal curves is given. And the convergence properties of this algorithm are analyzed. The new construction algorithm of principal curves is illustrated on some simulated data sets.
32435431
Regularized Semi-supervised Classification on Manifold
Semi-supervised learning gets estimated marginal distribution PX with a large number of unlabeled examples and then constrains the conditional probability p(y | x) with a few labeled examples. In this paper, we focus on a regularization approach for semi-supervised classification. The label information graph is first defined to keep the pairwise label relationship and can be incorporated with neighborhood graph which reflects the intrinsic geometry structure of PX. Then we propose a novel regularized semi-supervised classification algorithm, in which the regularization term is based on the modified Graph Laplacian. By redefining the Graph Laplacian, we can adjust and optimize the decision boundary using the labeled examples. The new algorithm combines the benefits of both unsupervised and supervised learning and can use unlabeled and labeled examples effectively. Encouraging experimental results are presented on both synthetic and real world datasets.
15310670
A Recent-Biased Dimension Reduction Technique for Time Series Data
There are many techniques developed for tackling time series and most of them consider every part of a sequence equally. In many applications, however, recent data can often be much more interesting and significant than old data. This paper defines new recent-biased measures for distance and energy, and proposes a recent-biased technique based on DWT for time series in which more recent data are considered more significant. With such a recent-biased technique, the dimension of time series can be reduced while effectively preserving the recent-biased energy. Our experiments have demonstrated the effectiveness of the proposed approach for handling time series.
14675910
Generalized dimension-reduction framework for recent-biased time series analysis
Recent-biased approximations have received increased attention recently as a mechanism for learning trend patterns from time series or data streams. They have shown promise for clustering time series and incrementally pattern maintaining. In this paper, we design a generalized dimension-reduction framework for recent-biased approximations, aiming at making traditional dimension-reduction techniques actionable in recent-biased time series analysis. The framework is designed in two ways: equi-segmented scheme and vari-segmented scheme. In both schemes, time series data are first partitioned into segments and a dimension-reduction technique is applied to each segment. Then, more coefficients are kept for more recent data while fewer kept for older data. Thus, more details are preserved for recent data and fewer coefficients are kept for the whole time series, which improves the efficiency greatly. We experimentally evaluate the proposed approach, and demonstrate that traditional dimension-reduction techniques, such as SVD, DFT, DWT, PIP, PAA, and landmarks, can be embedded into our framework for recent-biased approximations over streaming time series.
29045473
On Reduct Construction Algorithms
This paper critically analyzes reduct construction methods at two levels. At a high level, one can abstract commonalities from the existing algorithms, and classify them into three basic groups based on the underlying control structures. At a low level, by adopting different heuristics or fitness functions for attribute selection, one is able to derive most of the existing algorithms. The analysis brings new insights into the problem of reduct construction, and provides guidelines for the design of new algorithms
10152279
An on-chip IP address lookup algorithm
This paper proposes a new data compression algorithm to store the routing table in a tree structure using very little memory. This data structure is tailored to a hardware design reference model presented in this paper. By exploiting the low memory access latency and high bandwidth of on-chip memory, high-speed packet forwarding can be achieved using this data structure. With the addition of pipeline in the hardware, IP address lookup can only be limited by the memory access speed. The algorithm is also flexible for different implementation. Experimental analysis shows that, given the memory width of 144 bits, our algorithm needs only 400kb memory for storing a 20k entries IPv4 routing table and five memory accesses for a search. For a 1M entries IPv4 routing table, 9Mb memory and seven memory accesses are needed. With memory width of 1,068 bits, we estimate that we need 100Mb memory and six memory accesses for a routing table with 1M IPv6 prefixes.
16630001
Model-based Runtime Verification Framework for Self-optimizing Systems
This paper describes a novel on-line model checking approach offered as service of a real-time operating system (RTOS). The verification system is intended especially for self-optimizing component-based real-time systems where self-optimization is performed by dynamically exchanging components. The verification is performed at the level of (RT-UML) models. The properties to be checked are expressed by RT-OCL terms where the underlying temporal logic is restricted to either time-annotated ACTL or LTL formulae. The on-line model checking runs interleaved with the execution of the component to be checked in a pipelined manner. The technique applied is based on on-the-fly model checking. More specifically for ACTL formulae this means on-the-fly solution of the NHORNSAT problem while in the case of LTL the emptiness checking method is applied.
8373854
Scalably supporting durable subscriptions in a publish/subscribe system
We describe algorithms to scalably support durable subscriptions in a publish-subscribe system. Durable subscriptions are guaranteed exactly-once message delivery, despite periods of disconnection from the system. Our algorithms persistently log each message only once in the system, and can support administratively specified ’early-release’ policies that reclaim persistent storage in the presence of misbehaving durable subscribers. To efficiently recover messages missed by a disconnected durable subscriber, without refiltering messages published while the subscriber was disconnected, we persistently log filtering information in a manner optimized for the read/write pattern of durable subscriptions. Consolidation of data-structures across all subscribers that are done with catching up (after a disconnection), helps the system support a larger number of subscribers. We experimentally demonstrate the low-latency and scalability properties of our implementation, both in the presence and absence of failures.
8245310
Packet classification using independent sets
This paper describes a new algorithm for packet classification using the concept of independent sets. The algorithm has very small memory requirements. The search speed is neither sensitive to the rule table nor to the percentage of wildcards in the fields. It also scales well from two dimensional classifiers to high dimensional ones. In particular, the algorithm is inherently parallel. Hardware tailored to this algorithm can achieve very fast search speed.