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In this paper, we propose an efficient algorithm, called CMP-Miner, to mine closed patterns in a time-series database where each record in the database, also called a transaction, contains multiple time-series sequences. Our proposed algorithm consists of three phases. First, we transform each time-series sequence in a transaction into a symbolic sequence. Second, we scan the transformed database to find frequent patterns of length one. Third, for each frequent pattern found in the second phase, we recursively enumerate frequent patterns by a frequent pattern tree in a depth-first search manner. During the process of enumeration, we apply several efficient pruning strategies to remove frequent but non-closed patterns. Thus, the CMP-Miner algorithm can efficiently mine the closed patterns from a time-series database. The experimental results show that our proposed algorithm outperforms the modified Apriori and BIDE algorithms.  相似文献   
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In this paper, we propose mining frequent patterns from univariate uncertain data streams, which have a quantitative interval for each attribute in a transaction and a probability density function indicating the possibilities that the values in the interval appear. Many data streams comprise flows of univariate uncertain data, for example, the records of atmospheric pollution sensors, and network monitoring records. We propose two algorithms to address this issue: the ExactU2Stream algorithm and the ApproxiU2Stream algorithm. The former incrementally stores the incoming transactions, and delays the mining process until it is requested. The latter mines the transactions immediately when they arrive, and stores the derived frequent patterns. Compared with the latter, the former returns results that are more accurate, but it also requires more response time. Both algorithms utilize the sliding window scheme, which decomposes the continuous data stream into discrete, overlapping chunks. The proposed algorithms outperform the compared methods in terms of runtime and memory usage. We have applied the two proposed algorithms to the data streams recording the air quality in Taiwan; the derived frequent patterns not only show the common air quality in Taiwan but also show the extremely bad air quality when a sand storm affects Taiwan.  相似文献   
3.
We propose two methods to accelerate the matching of an unknown object with known objects, all of which are expressed as feature vectors. The acceleration becomes necessary when the population of known objects is large and a great deal of time would be required to match all of them. Our proposed methods are multiple decision trees and sub-vector matching, both of which use a learning procedure to estimate the optimal values of certain parameters. Online matching with a combination of the two methods is then performed, whereby candidates are matched rapidly without sacrificing the test accuracy. The process is demonstrated by experiments in which we apply the proposed methods to handwriting recognition and language identification. The speed-up factor of our approach is dramatic compared with an alternative approach that eliminates candidates in a deterministic fashion.  相似文献   
4.
In this paper, we propose a novel face detection method based on the MAFIA algorithm. Our proposed method consists of two phases, namely, training and detection. In the training phase, we first apply Sobel's edge detection operator, morphological operator, and thresholding to each training image, and transform it into an edge image. Next, we use the MAFIA algorithm to mine the maximal frequent patterns from those edge images and obtain the positive feature pattern. Similarly, we can obtain the negative feature pattern from the complements of edge images. Based on the feature patterns mined, we construct a face detector to prune non-face candidates. In the detection phase, we apply a sliding window to the testing image in different scales. For each sliding window, if the slide window passes the face detector, it is considered as a human face. The proposed method can automatically find the feature patterns that capture most of facial features. By using the feature patterns to construct a face detector, the proposed method is robust to races, illumination, and facial expressions. The experimental results show that the proposed method has outstanding performance in the MIT-CMU dataset and comparable performance in the BioID dataset in terms of false positive and detection rate.  相似文献   
5.
Mining frequent patterns from univariate uncertain data   总被引:1,自引:0,他引:1  
In this paper, we propose a new algorithm called U2P-Miner for mining frequent U2 patterns from univariate uncertain data, where each attribute in a transaction is associated with a quantitative interval and a probability density function. The algorithm is implemented in two phases. First, we construct a U2P-tree that compresses the information in the target database. Then, we use the U2P-tree to discover frequent U2 patterns. Potential frequent U2 patterns are derived by combining base intervals and verified by traversing the U2P-tree. We also develop two techniques to speed up the mining process. Since the proposed method is based on a tree-traversing strategy, it is both efficient and scalable. Our experimental results demonstrate that the U2P-Miner algorithm outperforms three widely used algorithms, namely, the modified Apriori, modified H-mine, and modified depth-first backtracking algorithms.  相似文献   
6.
The existing object recognition methods can be classified into two categories: interest-point-based and discriminative-part-based. The interest-point-based methods do not perform well if the interest points cannot be selected very carefully. The performance of the discriminative-part-base methods is not stable if viewpoints change, because they select discriminative parts from the interest points. In addition, the discriminative-part-based methods often do not provide an incremental learning ability. To address these problems, we propose a novel method that consists of three phases. First, we use some sliding windows that are different in scale to retrieve a number of local parts from each model object and extract a feature vector for each local part retrieved. Next, we construct prototypes for the model objects by using the feature vectors obtained in the first phase. Each prototype represents a discriminative part of a model object. Then, we establish the correspondence between the local parts of a test object and those of the model objects. Finally, we compute the similarity between the test object and each model object, based on the correspondence established. The test object is recognized as the model object that has the highest similarity with the test object. The experimental results show that our proposed method outperforms or is comparable with the compared methods in terms of recognition rates on the COIL-100 dataset, Oxford buildings dataset and ETH-80 dataset, and recognizes all query images of the ZuBuD dataset. It is robust enough for distortion, occlusion, rotation, viewpoint and illumination change. In addition, we accelerate the recognition process using the C4.5 decision tree technique, and the proposed method has the ability to build prototypes incrementally.  相似文献   
7.
In this paper, we propose a novel algorithm, called 9DSPA-Miner, to mine frequent patterns from an image database, where every image is represented by the 9D-SPA representation. Our proposed method consists of three phases. First, we scan the database once and create an index structure. Next, the index structure is scanned to find all frequent patterns of length two. Finally, we use the frequent k-patterns (k ? 2) to generate candidate (k + 1)-patterns and check if the support of each candidate generated is not less than the user-specified minimum support threshold by using the index structure. Then, the steps in the third phase are repeated until no more frequent patterns can be found. Since the 9DSPA-Miner algorithm uses the characteristics of the 9D-SPA representation to prune most of impossible candidates, the experiment results demonstrate that it is more efficient and scalable than the modified Apriori method.  相似文献   
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