441449
A novel ant clustering algorithm based on cellular automata
Based on the principle of cellular automata in artificial life, an artificial ant sleeping model (ASM) and an ant algorithm for cluster analysis (A4C) are presented. Inspired by the behaviors of gregarious ant colonies, we use the ant agent to represent a data object. In ASM, each ant has two states: a sleeping state and an active state. The ant's state is controlled by a function of the ant's fitness to the environment it locates and a probability for the ants becoming active. The state of an ant is determined only by its local information. By moving dynamically, the ants form different subgroups adaptively, and hence the data objects they represent are clustered. Experimental results show that the A4C algorithm on ASM is significantly better than other clustering methods in terms of both speed and quality. It is adaptive, robust and efficient, achieving high autonomy, simplicity and efficiency.
6316461
Partitioned optimization algorithms for multiple sequence alignment
Multiple sequence alignment is an important and difficult problem in molecular biology and bioinformatics. In this paper, we propose a partitioning approach that significantly improves the solution time and quality by utilizing the locality structure of the problem. The algorithm solves the multiple sequence alignment in three stages. First, an automated and suboptimal partitioning strategy is used to divide the set of sequences into several subsections. Then a multiple sequence alignment algorithm based on ant colony optimization is used to align the sequences of each subsection. Finally, the alignment of original sequences can be obtained by assembling the result of each subsection. The ant colony algorithm is highly optimized in order to avoid local optimal traps and converge to global optimal efficiently. Experimental results show that the algorithm can significantly reduce the running time and improve the solution quality on large-scale multiple sequence alignment benchmarks.
60542101
A novel ant clustering algorithm based on cellular automata
Based on the principle of cellular automata in artificial life, an artificial ant sleeping model (ASM) and an ant algorithm for cluster analysis (A4C) are presented. Inspired by the behaviors of gregarious ant colonies, we use the ant agent to represent a data object. In ASM, each ant has two states: a sleeping state and an active state. The ant's state is controlled by a function of the ant's fitness to the environment it locates and a probability for the ants becoming active. The state of an ant is determined only by its local information. By moving dynamically, the ants form different subgroups adaptively, and hence the data objects they represent are clustered. Experimental results show that the A4C algorithm on ASM is significantly better than other clustering methods in terms of both speed and quality. It is adaptive, robust and efficient, achieving high autonomy, simplicity and efficiency.
14133000
Software-based diagnosis for processors
Software-based self-test (SBST) is emerging as a promising technology for enabling at-speed test of high-speed microprocessors using low-cost testers. We explore the fault diagnosis capability of SBST, in which functional information can be used to guide and facilitate the generation of diagnostic tests. By using a large number of carefully constructed diagnostic test programs, the fault universe can be divided into fine-grained partitions, each corresponding to a unique pass/fail pattern. We evaluate the quality of diagnosis by constructing diagnostic-tree-based fault dictionaries. We demonstrate the feasibility of the proposed method by applying it to a processor example. Experimental results show its potential as an effective method for diagnosing larger processors.
44509988
A threshold criterion, auto-detection and its use in MST-based clustering
Clustering is to group data points into homogenous clusters so that data points within the same cluster are more similar than data points belonging to different clusters. There are many effective clustering algorithms for discovering arbitrary shaped clusters, but one common problem of many algorithms is the difficulty for users to decide appropriate parameters for these algorithms. To reduce the dependence of clustering performance on parameters, this paper proposes a threshold criterion for the single linkage cluster analysis and incorporates it into the Minimum Spanning Tree (MST) based clustering method. Since the threshold can be automatically decided according to the underlying data distributions, arbitrary shaped clusters can be discovered with little human intervention. The experimental results on spatial data are very encouraging.
40616325
A Color Bar Based Affective Annotation Method for Media Player
Multimedia technology has been developing quickly since the computer was invented. Nowadays, more and more people use media players to watch movies and TV shows on their computer. But it seems that we are too busy sometimes to spare nearly two hours watching a movie from beginning to end. We’d like to find and watch the most exciting episodes that we are interested in as soon as possible. The media players we use often only provide a slider bar for locating, which is rather low in efficiency. Respecting this, an affective annotation method for video clips is proposed in this paper and the method is implemented in an affectively annotated media player. In addition, two experiments were set up to evaluate the proposed method. Experiment results indicate that affective annotation facilitates the subjects in quick locating target events and helps them understand the scenarios better.
16583184
Using cluster skeleton as prototype for data labeling
A new approach, designed for clustering data whose underlying distribution shapes are arbitrary, is presented. This study is concerned with the use of the skeleton of a cluster as its prototype, which can represent the cluster more closely than that of using a single data point. The given data set is then partitioned into those skeleton-represented clusters without any prior knowledge nor assumptions of hidden structures. A novel function called cluster characteristic function (CCF) has been constructed and the associated theorems have been proposed and proved that the proper number of clusters can be determined with the approach.
1252625
Supporting self-adaptation in streaming data mining applications
There are many application classes where the users are flexible with respect to the output quality. At the same time, there are other constraints, such as the need for real-time or interactive response, which are more crucial. This paper presents and evaluates a runtime algorithm for supporting adaptive execution for such applications. The particular domain we target is distributed data mining on streaming data. This work has been done in the context of a middleware system called GATES (grid-based adaptive execution on streams) that we have been developing. The self-adaptation algorithm we present and evaluate in this paper has the following characteristics. First, it carefully evaluates the long-term load at each processing stage. It considers different possibilities for the load at a processing stage and its next stages, and decides if the value of an adaptation parameter needs to be modified, and if so, in which direction. To find the ideal new value of an adaptation parameter, it performs a binary search on the specified range of the parameter. To evaluate the self-adaptation algorithm in our middleware, we have implemented two streaming data mining applications. The main observations from our experiments are as follows. First, our algorithm is able to quickly converge to stable values of the adaptation parameter, for different data arrival rates, and independent of the specified initial value. Second, in a dynamic environment, the algorithm is able to adapt the processing rapidly. Finally, in both static and dynamic environments, the algorithm clearly outperforms the algorithm described in our earlier work and an obvious alternative, which is based on linear-updates.