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The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

In order to overcome the shortcomings of traditional reliability analysis based on large samples and statistical methods, a reliability evaluation method of the turboprop engine based on subspace similarity is proposed. The state matrix of vibration signals of the turboprop engine is obtained by the wavelet packet. The subspace of state matrix is constructed by kernel principal component analysis (KPCA). The kernel of the subspace inner product matrix is obtained based on singular value decomposition (SVD). The normalized first nuclear protagonist is treated as the operational reliability index. The method is validated by using the measured data of turboprop engines form an aircraft machinery factory. And it shows that the proposed method is reasonable and feasible, which provides an effective means for the reliability evaluation of machinery under the small sample condition. [ABSTRACT FROM AUTHOR]

We study the cluster ensemble problem and propose a cluster ensemble approach based on subspace similarity (CEASS). From a subspace similarity perspective, we seek the optimal subspace which is most similar to the given subspaces corresponding to the cluster solutions to be combined. We formulate the cluster ensemble problem as an optimization problem of minimizing the squared sum of Euclidean distances between the standard orthogonal basis vectors of the target subspace and the given subspaces. We derived an explicit solution to the preceding problem in terms of singular value decomposition. Moreover, the solution consists of the low dimensional embeddings of instances. Finally, K-means algorithm with the minimum-maximum principle is utilized to cluster instances according to their coordinates in the embedding space. In particular, we circumvent the initialization problem of K-means by employing CEASS that combines different K-means clustering solutions obtained from random initialization to obtain a stable clustering result. We evaluate and compare CEASS so constructed with several other state-of-art cluster ensemble algorithms using nine real world datasets. Experimental results demonstrate that CEASS generally outperforms other algorithms in terms of normalized mutual information and F1 measure. In addition, CEASS is extremely efficient compared to hierarchy clustering algorithms. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method. [ABSTRACT FROM AUTHOR]