This paper presents a method for analyzing time-series laboratory examination databases. The key concept of this method is classification of temporal patterns using multiscale structure matching and a rough set-based clustering method. Multiscale matching enables us to capture similarity between two sequences of examinations from both short-term and long-term points of view. The rough-set based...

Nowadays privacy issues are major concern for many government and other private organizations to delve important information from large repositories of data. Privacy preserving clustering which is one of the techniques emerged to addresses the problem of extracting useful clustering patterns from distorted data without accessing the original data directly. In this paper two hybrid data transfor...

This paper presents a novel hybrid data clustering algorithm based on parameter adaptive harmony search algorithm. The recently developed parameter adaptive harmony search algorithm (PAHS) is used to refine the cluster centers, which are further used in initializing Expectation-Maximization clustering algorithm. The optimal number of clusters are determined through four well-known cluster valid...

In this paper we present a method for the automatic term clustering. The method uses a hybrid similarity measure to cluster terms automatically extracted from a corpus by applying the C/NC value method. The measure comprises contextual, functional and lexical similarity, and it is used to instantiate the cell values in a similarity matrix. The clustering algorithm uses either the nearest neighb...

With the increasing of network attacks, network information security has become an issue of global concern. The problem with the mainstream intrusion detection system is the huge number of alarm information, it has high false positive rate. This paper presents a data mining technology to reduce false positive rate and improve the accuracy of detection. The technique is unsupervised clustering m...

In this paper we enhance the notion of anomaly detection and use both neural network (NN) and decision tree (DT) for intrusion detection. While DTs are highly successful in detecting known attacks, NNs are more interesting to detect new attacks. In our method we proposed a new approach to design the system using both DT and combination of unsupervised and supervised NN for Intrusion Detection S...

A challenge in hybrid evolutionary algorithms is to define efficient strategies to cover all search space, applying local search only in actually promising search areas. This paper proposes a way of detecting promising search areas based on clustering. In this approach, an iterative clustering works simultaneously to an evolutionary algorithm accounting the activity (selections or updatings) in...

A hybrid of two novel methods additive fuzzy spectral clustering and lifting method over a taxonomy is applied to analyse the research activities of a department. To be specific, we concentrate on the Computer Sciences area represented by the ACM Computing Classification System (ACM-CCS), but the approach is applicable also to other taxonomies. Clusters of the taxonomy subjects are extracted us...

Interactive clustering refers to situations in which a human labeler is willing to assist a learning algorithm in automatically clustering items. We present a related but somewhat different task, assisted clustering, in which a user creates explicit groups of items from a large set and wants suggestions on what items to add to each group. While the traditional approach to interactive clustering...

Hybrid information retrieval (IR) schemes combine di erent normalization techniques and similarity functions. Hybrid schemes provide an eÆcient technique to improve precision and recall (see e.g., [4]). This paper reports a hybrid clustering scheme that applies a singular value decomposition (SVD) based algorithm followed by a k{means type clustering algorithm. The output of the rst algorithm b...