Although Subsequence Time Series (STS) clustering is one of the most popular pattern discovery techniques from timeseries data, a mathematical methodology for analyzing STS clustering (or pattern discovery from time-series data) has attracted little attention. In the situation, it has had a surprising report [10] that cluster centers obtained using STS clustering closely resemble ”sine waves” w...

In this paper we presented a new approach to manipulate digital signal in order create features array, which can be used as signature retrieve the signal. Each is associated with local binary pattern (LBP) histogram; histogram will calculated based on LBP operator, then k-means clustering was generate required for each The proposed method implemented, tested and obtained experimental results we...

BACKGROUND Diet is an important modifiable risk factor for chronic diseases. In the search for effective strategies to improve dietary patterns in order to promote healthy ageing, new approaches considering contextual factors in public health medicine are warranted. The aim of this study is to examine the spatial clustering of dietary patterns in a large representative sample of adults. METHO...

It is well-known that multidimensional indices are efficient to improve the query performance on relational data. As one successful multi-dimensional index structure, R*-tree, a famous member of the R-tree family, is very popular. The clustering pattern of the objects (i.e., tuples in relational tables) among R*-tree leaf nodes is one of the deceive factors on performance of range queries, a po...

Artificial Immune Systems (AIS) can be defined as soft computing systems inspired by immune system of vertebrates. Immune system is an adaptive pattern recognition system. AIS have been used in pattern recognition, machine learning, optimization and clustering. Feature reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encoun...

To satisfy the robust requirement when designing fault identifying method, this paper proposes a novel method to identify sensor fault. Conventional fault identifying method could only classify fault into explicit set. Yet, when a novel faulty pattern occurs, the conventional method can not identify this new pattern and will classify it into a set known ahead of time. For the purpose of robustn...

This paper describes a new approach to the segmentation of textured gray-scale images based on image pre-"ltering and fractal features. Traditionally, "lter bank decomposition methods consider the energy in each band as the textural feature, a parameter that is highly dependent on image intensity. In this paper, we use fractal-based features which depend more on textural characteristics and not...