Outlier detection in a data set is very important in performing proper data mining. In this paper, we propose a method for efficiently detecting outliers by performing cluster analysis using the DS algorithm improved from the k-means algorithm. This method is simpler to detect outliers than traditional methods, and these detected outliers can quantitatively indicate “the degree of outlier”. Using this method, we detect abnormal trading days from OHLCs for S&P500 and FTSA, which are typical and world-wide stock indexes, from the beginning of 2005 to the end of 2015. They are defined as non-steady trading days, and the conditions for becoming the non-steady markets are mined as new knowledge. Applying the mined knowledge to OHLCs from the beginning of 2016 to the end of 2018, we can predict the non-steady trading days during that period. By verifying the predicted content, we show the fact that the appropriate knowledge has been successfully mined and show the effectiveness of the outlier detection method proposed in this paper. Furthermore, we mutually reference and comparatively analyze the results of applying this method to multiple stock indexes. This analyzes possible to visualize when and where social and economic impacts occur and how they propagate through the earth. This is one of the new applications using this method.

A packet network architecture called “functionally distributed transport networking” is being studied, where control elements (CEs) are separated from the forwarding elements (FEs) of all routers in a network, and a centralized CE manages the control functions for all FEs. A crucial issue to be addressed in this network architecture is the occurrence of bottlenecks in the CE performance, and rapid network restoration after failures is the main problem to be solved. Thus, we propose here a fast backup route determination method suitable for this network architecture, and we also show the practicality of this architecture. Most failures can be categorized as single-node or single-link failures. The proposed method prepares backup routes for all possible single-node failures in advance and computes backup routes for single-link failures after the failure occurs. The number of possible single-node failures is much less than that of possible single-link failures, and the preparation of backup routes for single-node failures is practical under the memory requirements. Two techniques are used in computing backup routes for single-link failures in order to reduce the computation time. One is to calculate only the routes affected by the link failure. The other is to use an algorithm to compute backup routes for single-link failures based on preplanned backup routes for single-node failures. To demonstrate the practicality of our method, we evaluated the amount of memory and computation time needed to prepare backup routes for all single-node failures, and we carried out simulations with various network topologies to evaluate the route computation time required for a single-link failure.

This study investigates the effect of the method of time division in frequency domain ICA on estimation accuracy of ICA. We show that source signals expressed in the frequency domain lose non-Gaussianity and independence because of the long and overlapping window function, respectively, in time division. Consequently, the estimation accuracy of ICA decreases.

ATM technology can integrate not only new multimedia services but also existing services such as plain old telephone services (POTS) and narrow-band ISDN (N-ISDN) services. We developed ATM subscriber line interface circuits (ATM-SLIC), which can accommodate POTS or N-ISDN subscribers, and a trial ATM access switching system using the ATM-SLICs. This paper proposes embedded trunk functions, a message-type signaling transfer method, an economical ATM interface structure, and a cell-based system control method. Moreover, it shows the main characteristics and efficiency of these proposals in terms of experimental data.