Assuming that actors u and v have r common neighbors in a social network we are interested in how likely is that u and v are adjacent. This question is addressed by studying the collection of conditional probabilities, denoted cl(r), r = 0, 1, 2, . . . , that two randomly chosen actors of the social network are adjacent, given that they have r common neighbors. The function r → cl(r) describes ...

Clustering at multiple critical scales may be common for plants since many different factors and processes may cause clustering. This is especially true for tropical rain forests for which theories explaining species coexistence and community structure rest heavily on spatial patterns. We used point pattern analysis to analyze the spatial structure of Shorea congestiflora, a dominant species in...

introduction thunderstorms pose a significant threat to modern societies and their assets. despite their local-scale characteristics, severe thunderstorms and associated extreme events like heavy rainfall, hail, gusts, or tornadoes can cause considerable damage to agriculture, buildings, or infrastructure, and facilities. thunderstorms are highly localized and largely stationary weather systems...

In this paper, we demonstrate how PCA and ICA can be used for embedding graphs in pattern-spaces. Graph spectral feature vectors are calculated from the leading eigenvalues and eigenvectors of the unweighted graph adjacency matrix. The vectors are then embedded in a lower dimensional pattern space using both the PCA and ICA decomposition methods. Synthetic and real sequences are tested using th...

Mining user patterns of log files can provide significant and useful informative knowledge. This paper present an approach for mining similarity of interest among web users from their past access behaviors. Unlike traditional clustering methods that focus on grouping objects with similar values on a set of dimensions, clustering by pattern similarity finds objects that exhibit a coherent patter...

This research proposes a new strategy where documents are encoded into string vectors and modified version of k means algorithm to be adaptable to string vectors for text clustering. Traditionally, when k means algorithm is used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classifi...

finding repetitive subsequences in genome is a challengeable problem in bioinformatics research area. a lot of approaches have been proposed to solve the problem, which could be divided to library base and de novo methods. the library base methods use predetermined repetitive genome’s subsequences, where library-less methods attempt to discover repetitive subsequences by analytical approaches. ...

Nowadays, Information retrieval plays an important role in the web. Many researches presented techniques for information retrieval process from databases. The previous work presented extended tree pattern clustering process for XML massive storages. This paper presents a new technique termed semantic data clustering (SDC) technique for combining the Data warehouse and web data for OLAP by retri...

Data clustering is a process of arranging similar data into groups. Data clustering is a common technique for data analysis and is used in many fields, including data mining, pattern recognition and image analysis. In this paper a hybrid clustering algorithm based on K-mean is described. K-means clustering is a common and simple approach for data clustering but this method has some limitation s...

In knowledge discovery, data mining of time series information has many important applications. Especially, sequential patterns and periodic patterns, which evolved from the association rule, have been applied in many useful practices. This paper presents another useful concept, the periodic clustering sequential (PCS) pattern, which uses clustering to mine valuable information from temporal or...