Writer Identification for chinese handwriting

Abstract Chinese handwriting identification has become a hot research in pattern recognition and image processing. In this paper, we present overview of relevant papers from the previous related studies until to the recent publications regarding to the Chinese Handwriting Identification. The strength, weaknesses, accurateness and comparison of well known approaches are reviewed, summarized and documented. This paper provides broad spectrum of pattern recognition technology in assisting writer identification tasks, which are at the forefront of forensic and biometrics based on identification application

Scalable And Efficient Outlier Detection In Large Distributed Data Sets With Mixed-type Attributes

An important problem that appears often when analyzing data involves identifying irregular or abnormal data points called outliers. This problem broadly arises under two scenarios: when outliers are to be removed from the data before analysis, and when useful information or knowledge can be extracted by the outliers themselves. Outlier Detection in the context of the second scenario is a research field that has attracted significant attention in a broad range of useful applications. For example, in credit card transaction data, outliers might indicate potential fraud; in network traffic data, outliers might represent potential intrusion attempts. The basis of deciding if a data point is an outlier is often some measure or notion of dissimilarity between the data point under consideration and the rest. Traditional outlier detection methods assume numerical or ordinal data, and compute pair-wise distances between data points. However, the notion of distance or similarity for categorical data is more difficult to define. Moreover, the size of currently available data sets dictates the need for fast and scalable outlier detection methods, thus precluding distance computations. Additionally, these methods must be applicable to data which might be distributed among different locations. In this work, we propose novel strategies to efficiently deal with large distributed data containing mixed-type attributes. Specifically, we first propose a fast and scalable algorithm for categorical data (AVF), and its parallel version based on MapReduce (MR-AVF). We extend AVF and introduce a fast outlier detection algorithm for large distributed data with mixed-type attributes (ODMAD). Finally, we modify ODMAD in order to deal with very high-dimensional categorical data. Experiments with large real-world and synthetic data show that the proposed methods exhibit large performance gains and high scalability compared to the state-of-the-art, while achieving similar accuracy detection rates

Toward Unsupervised Correlation Preserving Discretization

Abstract—Discretization is a crucial preprocessing technique used for a variety of data warehousing and mining tasks. In this paper, we present a novel PCA-based unsupervised algorithm for the discretization of continuous attributes in multivariate data sets. The algorithm leverages the underlying correlation structure in the data set to obtain the discrete intervals and ensures that the inherent correlations are preserved. Previous efforts on this problem are largely supervised and consider only piecewise correlation among attributes. We consider the correlation among continuous attributes and, at the same time, also take into account the interactions between continuous and categorical attributes. Our approach also extends easily to data sets containing missing values. We demonstrate the efficacy of the approach on real data sets and as a preprocessing step for both classification and frequent itemset mining tasks. We show that the intervals are meaningful and can uncover hidden patterns in data. We also show that large compression factors can be obtained on the discretized data sets. The approach is task independent, i.e., the same discretized data set can be used for different data mining tasks. Thus, the data sets can be discretized, compressed, and stored once and can be used again and again. Index Terms—Data preprocessing, principal component analysis, data mining/summarization, missing data, data compression.