Xizhe Zhang

Combining Multiple Clustering Methods Based on Core Group

As an unsupervised technique, clustering analysis has been widely applied in various fields. However, it is usually difficult to select an appropriate clustering method for an application, while no clustering method is suitable for all situations. This paper proposes a novel method to combine multiple clustering methods. First, the paper combines different agglomerative hierarchical methods in one clustering process to obtain core groups. Core group refers to the data that are always clustered together no matter what clustering method is applied. Then, it adopts other kind of clustering methods to refine the core groups and index database. In addition to conduct a series of experiments on the datasets from UCI, the paper applies the proposed method in a new research field, 3D model retrieval, to analyze and index the 3D model database.

Driver graph: the hidden geometry in controlling complex networks

The ability to control a complex network towards a desired behavior relies on our understanding of the complex nature of these social and technological networks. The existence of numerous control schemes in a network promotes us to wonder: what is the underlying relationship of all possible input nodes? Here we introduce input graph, a simple geometry that reveals the complex relationship between all control schemes and input nodes. We prove that the node adjacent to an input node in the input graph will appear in another control scheme, and the connected nodes in input graph have the same type in control, which they are either all possible input nodes or not. Furthermore, we find that the giant components emerge in the input graphs of many real networks, which provides a clear topological explanation of bifurcation phenomenon emerging in dense networks and promotes us to design an efficient method to alter the node type in control. The findings provide an insight into control principles of complex networks and offer a general mechanism to design a suitable control scheme for different purposes.

XML clustering based on common neighbor

Clustering on XML documents is an important task. However, it is difficult to select the appropriate parameters’ value for the clustering algorithms. By integrating outlier detection with clustering, the paper takes a new approach for analyzing the XML documents by structure distance. After stating the XML tree distance, the paper proposes a new clustering algorithm, which stops clustering automatically by utilizing the outlier information and needs only one parameter, whose appropriate value range can be decided in the outlier mining process. The paper adopts the XML dataset with different structure and other real-life datasets to compare it with other clustering algorithms.

Efficient target control of complex networks based on preferential matching

Controlling a complex network towards a desired state is of great importance in many applications. Existing works present an approximate algorithm to find the input nodes used to control partial nodes of the network. However, the input nodes obtained by this algorithm depend on the node matching order and cannot achieve optimum results. Here we present a novel algorithm to find the input nodes for target control based on preferential matching. The algorithm elaborately arranges the matching order of the nodes to reduce the size of the input node set. The results on both synthetic and real networks indicate that the proposed algorithm outperforms the previous algorithm.

Distance Ratio Fractal for Complex Mapping z\leftarrow z^a

The complex mapping z larr za is important in dynamical application, but it is not received as much attention in the literature as the mapping z larr za+c, for there is not fractal structure by using escape time algorithm. This paper utilizes a new method named as distance ratio iteration method and discusses the iteration properties of the complex mapping z larr za. The distance ratio iteration method can render the convergence region of the mapping, so the image has complex and self-similarity structure. This paper generates fractal image using distance ratio iteration method for various exponents of z larr za and discusses their visual properties. There is rich detail fractal structure in the mapping z larr za

Clustering XML documents by structure based on common neighbor

It is important to perform the clustering task on XML documents. However, it is difficult to select the appropriate parameters’ value for the clustering algorithms. Meanwhile, current clustering algorithms lack the effective mechanism to detect outliers while treating outliers as “noise”. By integrating outlier detection with clustering, the paper takes a new approach for analyzing the XML documents by structure. After stating the concept of common neighbor based outlier, the paper proposes a new clustering algorithm, which stops clustering automatically by utilizing the outlier information and needs only one parameter, whose appropriate value range is decided in the outlier mining process. After discussing some features of the proposed algorithm, the paper adopts the XML dataset with different structure and other real-life datasets to compare it with other clustering algorithms.