Intelligent Information Systems 

About: Intelligent Information Systems is an academic conference. The conference publishes majorly in the area(s): Cluster analysis & Artificial neural network. Over the lifetime, 1861 publications have been published by the conference receiving 37757 citations.

On Clustering Validation Techniques

Abstract: Cluster analysis aims at identifying groups of similar objects and, therefore helps to discover distribution of patterns and interesting correlations in large data sets. It has been subject of wide research since it arises in many application domains in engineering, business and social sciences. Especially, in the last years the availability of huge transactional and experimental data sets and the arising requirements for data mining created needs for clustering algorithms that scale and can be applied in diverse domains. This paper introduces the fundamental concepts of clustering while it surveys the widely known clustering algorithms in a comparative way. Moreover, it addresses an important issue of clustering process regarding the quality assessment of the clustering results. This is also related to the inherent features of the data set under concern. A review of clustering validity measures and approaches available in the literature is presented. Furthermore, the paper illustrates the issues that are under-addressed by the recent algorithms and gives the trends in clustering process.

Efficient and effective querying by image content

Abstract: In the QBIC (Query By Image Content) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, shape, position, and dominant edges of image objects and regions. Potential applications include medical (“Give me other images that contain a tumor with a texture like this one”), photo-journalism (“Give me images that have blue at the top and red at the bottom”), and many others in art, fashion, cataloging, retailing, and industry. We describe a set of novel features and similarity measures allowing query by image content, together with the QBIC system we implemented. We demonstrate the effectiveness of our system with normalized precision and recall experiments on test databases containing over 1000 images and 1000 objects populated from commercially available photo clip art images, and of images of airplane silhouettes. We also present new methods for efficient processing of QBIC queries that consist of filtering and indexing steps. We specifically address two problems: (a) non Euclidean distance measures; and (b) the high dimensionality of feature vectors. For the first problem, we introduce a new theorem that makes efficient filtering possible by bounding the non-Euclidean, full cross-term quadratic distance expression with a simple Euclidean distance. For the second, we illustrate how orthogonal transforms, such as Karhunen Loeve, can help reduce the dimensionality of the search space. Our methods are general and allow some “false hits” but no false dismissals. The resulting QBIC system offers effective retrieval using image content, and for large image databases significant speedup over straightforward indexing alternatives. The system is implemented in X/Motif and C running on an RS/6000.

WEKA: a machine learning workbench

Abstract: WEKA is a workbench for machine learning that is intended to aid in the application of machine learning techniques to a variety of real-world problems, in particular, those arising from agricultural and horticultural domains. Unlike other machine learning projects, the emphasis is on providing a working environment for the domain specialist rather than the machine learning expert. Lessons learned include the necessity of providing a wealth of interactive tools for data manipulation, result visualization, database linkage, and cross-validation and comparison of rule sets, to complement the basic machine learning tools. >

Adept _flex —Supporting Dynamic Changes of Workflows Without Losing Control

Abstract: Today‘s workflow management systems (WFMS_s) are only applicable in a secure and safe manner if the business process (BP) to be supported is well-structured and there is no need for ad hoc deviations at run-time. As only few BPs are static in this sense, this significantly limits the applicability of current workflow (WF) technology. On the other hand, to support dynamic deviations from premodeled task sequences must not mean that the responsibility for the avoidance of consistency problems and run-time errors is now completely shifted to the (naive) end user. In this paper we present a formal foundation for the support of dynamic structural changes of running WF instances. Based upon a formal WF model (ADEPT), we define a complete and minimal set of change operations (ADEPT_flex) that support users in modifying the structure of a running WF, while maintaining its (structural) correctness and consistency. The correctness properties defined by ADEPT are used to determine whether a specific change can be applied to a given WF instance or not. If these properties are violated, the change is either rejected or the correctness must be restored by handling the exceptions resulting from the change. We discuss basic issues with respect to the management of changes and the undoing of temporary changes at the instance level. Recently we have started the design and implementation of ADEPT_workflow, the ADEPT workflow engine, which will make use of the change facilities presented in this paper.

Andromaly: a behavioral malware detection framework for android devices

Abstract: This article presents Andromaly--a framework for detecting malware on Android mobile devices. The proposed framework realizes a Host-based Malware Detection System that continuously monitors various features and events obtained from the mobile device and then applies Machine Learning anomaly detectors to classify the collected data as normal (benign) or abnormal (malicious). Since no malicious applications are yet available for Android, we developed four malicious applications, and evaluated Andromaly's ability to detect new malware based on samples of known malware. We evaluated several combinations of anomaly detection algorithms, feature selection method and the number of top features in order to find the combination that yields the best performance in detecting new malware on Android. Empirical results suggest that the proposed framework is effective in detecting malware on mobile devices in general and on Android in particular.