BACKGROUND: Cell-scaffold contact measurements are derived from pairs of co-registered volumetric fluorescent confocal laser scanning microscopy (CLSM) images (z-stacks) of stained cells and three types of scaffolds (i.e., spun coat, large microfiber, and medium microfiber). Our analysis of the acquired terabyte-sized collection is motivated by the need to understand the nature of the shape dimensionality (1D vs 2D vs 3D) of cell-scaffold interactions relevant to tissue engineers that grow cells on biomaterial scaffolds. RESULTS: We designed five statistical and three geometrical contact models, and then down-selected them to one from each category using a validation approach based on physically orthogonal measurements to CLSM. The two selected models were applied to 414 z-stacks with three scaffold types and all contact results were visually verified. A planar geometrical model for the spun coat scaffold type was validated from atomic force microscopy images by computing surface roughness of 52.35 nm ±31.76 nm which was 2 to 8 times smaller than the CLSM resolution. A cylindrical model for fiber scaffolds was validated from multi-view 2D scanning electron microscopy (SEM) images. The fiber scaffold segmentation error was assessed by comparing fiber diameters from SEM and CLSM to be between 0.46% to 3.8% of the SEM reference values. For contact verification, we constructed a web-based visual verification system with 414 pairs of images with cells and their segmentation results, and with 4968 movies with animated cell, scaffold, and contact overlays. Based on visual verification by three experts, we report the accuracy of cell segmentation to be 96.4% with 94.3% precision, and the accuracy of cell-scaffold contact for a statistical model to be 62.6% with 76.7% precision and for a geometrical model to be 93.5% with 87.6% precision. CONCLUSIONS: The novelty of our approach lies in (1) representing cell-scaffold contact sites with statistical intensity and geometrical shape models, (2) designing a methodology for validating 3D geometrical contact models and (3) devising a mechanism for visual verification of hundreds of 3D measurements. The raw and processed data are publicly available from https://isg.nist.gov/deepzoomweb/data/ together with the web -based verification system.

This poster presents the problem of 3D contact measurements from two co-registered volumetric images (z-stacks). The 3D contact measurement consists of (a) segmenting an object of interest in each z-stack, (b) computing the relative spatial positions of the detected objects to detect contacts, (c) validating the accuracy of segmentation, and (d) visually verifying correct contact detection. The 3D measurement has to overcome challenges related to (1) intensity bleed-through across co-registered volumes, (2) insufficient knowledge about statistics and geometry of objects, (3) large RAM requirements (~3GB just to load the input data) and data volume (>1TB), and (4) complexity of 3D visual inspection.

The paper addresses the problem of understanding quality of image measurements extracted using widely used software libraries from large images. Image measurements (features) are extracted using software packages that vary in terms of programming languages, theoretical formulas for the same image feature, algorithmic implementations, input parameters, units of measurements, and definitions of image regions of interest. Our motivation is to quantify numerical variability of image features across software packages and determine image accuracy with respect to reference images. In addition, our objective is to enable scientists to extract any image features of interest from heterogeneous software libraries and gain provenance of every extracted numerical feature value. The provenance information is critical to achieve traceability of computations in terascale imaging. We pursue this objective by designing a client-server system that integrates image feature extractions from open source libraries such as ImageJ/Fiji, Python (scikit-image), CellProfiler, and in-house Java software packages. The system becomes useful for evaluating quality of image measurements, leveraging distributed computational resources for feature computations over big image data, sharing resulting feature values, and reproducing the feature values based on provenance. As an application of the designed system, we report the quality evaluations of 319 image features extracted using ImageJ/Fiji, Python (scikit-image), CellProfiler and in-house Java software packages with 43 duplicate features across the four packages. Using the normalized difference as metric, we identified 6 out of the 43 common features to differ over 1% in value and discuss the sources of these numerical differences.

Summary form only given. Motivation: Automated microscopy enables scientists to image an area of an experimental sample that is much larger than the microscope's Field of View (FOV) and to carry out time-lapse studies of cell cultures. An automated microscope acquires these images by generating a grid of partially overlapping images. This process generates hundreds to hundreds of thousands of image tiles that need to be stitched into a wide image. We address the problem of creating image mosaics from a grid of overlapping tiles constrained to only translational offsets. The challenges of creating a large mosaic image are: (1) sensitivity to image features in the overlapping regions of adjacent tiles (e.g., during the early period of cell colony growth), (2) computational requirements needed to assemble the resulting mosaic image, and (3) absence of ground truth needed for evaluating the accuracy of a stitching method. Results: This paper describes a stitching method called MIST (Microscopy Image Stitching Tool) with minimized translational uncertainty for large collections of grid-based microscopy tiles. The method improves tile translations computed using a registration method, such as the Fourier transform based phase correlation, by optimizing the normalized cross correlation between the overlap of adjacent tiles. The optimization incorporates mechanical properties of a microscope stage to filter translations with high errors. We estimate the microscope stage repeatability from the computed translations of the grid-based image tiles and then improve all translations using constrained Hill Climbing restricted to searching a square area of 4 times the stage repeatability per side. We also present a methodology for evaluating stitching accuracy based on creating reference centroid distance and area measurements of regions of interests that fit inside one FOV. The regions of interests (ROI) are segmented first and their mutual centroid distances and areas are measured using the microscope stage coordinates. The stitching accuracy is quantified by comparing the reference measurements to the measurements obtained by stitching a set of grid-based tiles by means of four NIST -derived metrics: false positive (added ROIs), false negative (undetected ROI), centroid distance error and area error. Following this methodology, we prepared three large reference datasets of stem cell colonies with low colony seeding which result in high uncertainty associated with the translation offsets. MIST generated a stitched image with an average colony centroid distance error less than 2 % that of a field of view and an average area error of 5 %. The sources of these errors include mechanical uncertainties, sample photobleaching, segmentation and stitching. We also show that the area error is mainly due to photobleaching and not stitching. We compared MIST stitching to the top five popular methods used in the literature. MIST produced the most accurate stitching result among all methods. Conclusions: MIST is an accurate stitching tool that can be applied to grid-based tiles with unknown translational offsets. Its performance-oriented implementation yields a fast execution time that makes the algorithm suitable for creating large mosaics (up to TBs in size). The evaluation methodology for stitching accuracy along with NIST -derived four performance metrics provides a general approach to characterize stitching algorithm performance. The application of the methodology in our case generated three reusable reference datasets with cell colonies. Availability: MIST is available as a Matlab executable or an ImageJ plugin. MIST ImageJ plugin has a CPU and a GPU implementation. All the information regarding this tool and its source code can be found at the following link: https://isg.nist.gov/.

BACKGROUND: The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements. METHODS: We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories. RESULTS: The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue. CONCLUSIONS: The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html.

In order to characterize the growth and properties of stem cell colonies with high statistical significance, we address the problem of applying machine learning and interactive web visualizations to TB-sized images. Overcoming the computational challenges and designing advanced visualizations enabled knowledge discovery and quantitative analysis at unprecedented scales in cell microscopy.

Our objective is to lower the barrier of executing spatial image computations in a computer cluster/cloud environment instead of in a desktop/laptop computing environment. We research two related problems encountered during an execution of spatial computations over terabyte-sized images using Apache Hadoop running on distributed computing resources. The two problems address (a) detection of spatial computations and their parameter estimation from a library of image processing functions, and (b) partitioning of image data for spatial image computations on Hadoop cluster/cloud computing platforms in order to minimize network data transfer. The first problem is solved by designing an iterative estimation methodology. The second problem is formulated as an optimization over three partitioning schemas (physical, logical without overlap and logical with overlap), and evaluated over several system configuration parameters. Our experimental results for the two problems demonstrate 100% accuracy in detecting spatial computations in the Java Advanced Imaging and ImageJ libraries, a speed-up of 5.36 between the default Hadoop physical partitioning and developed logical image partitioning with overlap, and 3.14 times faster execution of logical partitioning with overlap than the one without overlap. The novelty of our work is in designing an extension to Apache Hadoop to run a class of spatial image processing operations efficiently on a distributed computing resource.

We present a characterization of four basic Terabyte-sized image computations on a Hadoop cluster in terms of their relative efficiency according to the modified Amdahl's law. The work is motivated by the lack of standard benchmarks and stress tests for big image processing operations on a Hadoop computer cluster platform. Our benchmark design and evaluations were performed on one of the three microscopy image sets, each consisting of over one half Terabyte. All image processing benchmarks executed on the NIST Raritan cluster with Hadoop were compared against baseline measurements, such as the Terasort/Teragen designed for Hadoop testing previously, image processing executions on a multiprocessor desktop and on NIST Raritan cluster using Java Remote Method Invocation (RMI) with multiple configurations. By applying our methodology to assessing efficiencies of computations on computer cluster configurations, we could rank computation configurations and aid scientists in measuring the benefits of running image processing on a Hadoop cluster.

This work addresses the problem of re-projecting a terabyte-sized 3D data set represented as a set of 2D Deep Zoom pyramids. In general, a re-projection for small 3D data sets is executed directly in RAM. However, RAM becomes a limiting factor for terabyte-sized 3D volumes formed by a stack of hundreds of megapixel to gigapixel 2D frames. We have benchmarked three methods to perform the re-projection computation in order to overcome the RAM limitation.

This paper aims to provide GPU acceleration of an decision support for selecting software and hardware architecture for content-based document comparison. We evaluate Java, C, CUDA C and OpenCL implementations of an image characterization algorithm used for content-based document comparison on a CPU and NVIDIA and AMD graphics processing units (GPUs). Based on our experimental results, we conclude that the original Java implementation of the image characterization algorithm running on a CPU-based architecture can be accelerated by a factor of 6 if the Java code is re-implemented in C, or by a factor of almost 16 if the Java code is re-implemented in CUDA C and run on NVIDIA GTX 480 GPU hardware. We also provide a power efficiency analysis.

In this paper we describe a Software Server, a background process that in conjunction with a central repository of lightweight wrapper scripts allows functionality within heterogeneous software to be called in a simple and consistent manner. The key role of the Software Server is to provide a common interface to software functionality in a manner that can be programmed against, in essence re-introducing an API to compiled code. Using the Java restlet framework, we provide a Restful interface consisting of URL endpoints allowing any programming/scripting language capable of accessing URLs to utilize software functionality as a black box. In addition to being widely accessible the Restful interface allows for a secondary role from Software Servers by giving them the ability to turn any traditional desktop software into a cloud based web service. In this paper we describe these Software Servers, the scripts we use to wrap primarily GUI based software, and show how these servers allow software to be called and interconnected into workflows across distributed machines. Finally, quantitative experiments showing the feasibility of the described Software Servers on a number of applications are presented.

SUMMARY The Tupelo semantic content management middleware implements Knowledge Spaces that enable scientists to integrate information into a comprehensive research record as they work with existing tools and domain‐specific applications. Knowledge Spaces combine approaches that have demonstrated success in automating parts of this integration activity, including content management systems for domain‐neutral management of data, workflow technologies for management of computation and analysis, and semantic web technologies for extensible, portable, citable management of descriptive information and other metadata. Tupelo's ‘Context’ facility and its associated semantic operations both allow existing data representations and tools to be plugged in, and also provide a semantic ‘glue’ of important associative relationships that span the research record, such as provenance, social networks, and annotation. Tupelo has enabled the recent work creating e‐Science cyberenvironments to serve distributed, active scientific communities, allowing researchers to develop, coordinate and share datasets, documents, and computational models, while preserving process documentation and other contextual information needed to produce an integrated research record suitable for distribution and archiving. Copyright © 2011 John Wiley & Sons, Ltd.

This paper addresses the problem of designing a universal file format converter. File format conversion is a necessary part of data dissemination and curation. Complete and robust converters however are hard to find and build due to the abundance of file formats, the fact that many formats are closed, and the complexities within individual format specifications. On the other hand many software applications exist that are capable of performing some degree of data conversion between a subset of the available formats. To take advantage of this we introduce a data structure called an I/O-Graph to store the available input and output formats of these applications. Based on a concept of imposed code reuse we use this to develop a service, NCSA Polyglot, which through this graph is capable of performing the larger union of conversions supported by the underlying software. The Polyglot system is designed to be easily extensible, scalable with the number of conversion requests, and inclusive of all available third party software. Given a data set of files from a particular domain, we are able to assign weights to the edges within the I/O-Graph indicating the amount of information retained during a conversion. These edge weights allow the system to then choose conversion paths with the least amount of information loss.

This paper addresses the problem of discovering temporal and contextual relationships across document, data, and software categories of electronic records. We designed a methodology to discover unknown relationships by conducting file system and file content analyses. The work also investigates automation of metadata extraction from engineering drawings and storage requirements for metadata extraction. The methodology has been applied to extracting information from a test collection of electronic records about the NAVY ship (TWR 841) archived by the US National Archive (NARA). This test collection represents a problem of unknown relationships among files that include 784 2D image drawings and 22 CAD models.

In human health and performance studies, there is a need to detect, track and describe human subjects in real-time and in three-dimensional (3D) space. The work presented here is motivated by the problem of detecting human subjects in physical spaces, reconstructing their location in 3D, describing their spectral characteristics at each 3D location, and rendering 3D and spectral characteristics in real time. In this paper, we address the problem of integrating thermal infrared (IR) cameras and visible spectral cameras so that the 3D and spectral characteristics can be acquired in real time. We report performance tradeoffs associated with the integration of thermal-infrared and visible spectrum cameras and the discuss implications of our design for human health and performance studies.

This paper presents (a) new technologies for real-time immersion of humans into virtual reality (VR) environments with non-invasive real-time three-dimensional (3D) imaging, (b) a new methodology for evaluating immersive VR spaces in rehabilitation applications, and (c) experimental results documenting the benefits of Immersive VR (IVR) spaces for regaining proprioception. Our work focuses on designing immersive VR spaces with non-invasive multimedia sensory inputs where real time digital clones of humans are fused with virtual scenes for rehabilitation purposes. We hypothesize that humans with proprioceptive impairments can use their other senses as the proprioceptive feedback from real-time 3D+color reconstructions of their bodies in space. The objective is to investigate this hypothesis and quantify any benefits of immersive environments for regaining proprioception as one example of a rehabilitation application. The paper describes (a) the portable immersive VR system for real time 3D imaging, reconstruction and rendering, (b) a new methodology for quantitative evaluations of rehabilitation experiments in immersive VR spaces, and (c) the experimental results obtained for validating the above hypothesis with wheelchair basketball athletes. The novelty of the work lies in the first of its kind evaluation of the benefits of immersive VR spaces with multimedia cues for regaining proprioception.

As scientific work becomes more complex, contextual information is critical to enable its use. We have developed the concept of Knowledge Spaces that manage such information and scope the space of data, metadata, and algorithms relevant to the work of a Virtual Organization. This enables scientists to produce and manage descriptive metadata, process description, data provenance, as well as VO-specific quality assertions, vocabulary mappings, inferences, computation, approximations, and security policies. We have developed middleware and cooperating applications that demonstrate the potential of Knowledge Spaces in the production and dissemination of scientific data.

We present a new paradigm for publishing scientific workflows to the Web. Publishing in this system involves not only creating a Web space for running the workflow on demand, but also the ability for the publisher to convey expertise about its use by providing ways to focus attention on problem-relevant parameters, inputs and outputs. As part of the publishing step we include the ability to configure visualizations for specific outputs of the workflow. The resulting system is a Web based environment focused on the ability to publish existing workflows so that other users in the community can execute those workflows and visualize the results from the Web. Workflow executions and visualizations can be shared with the community along with workflow definitions.

This work addresses the problem of designing a scalable framework for archival appraisals of contemporary PDF documents. The motivation for our work is to provide an e-science solution that (a) fuses the independent research methodologies focusing on specific information types to one comprehensive analytical framework, (b) optimizes tradeoffs between computational requirements and preservation costs, and (b) bridges the small scale and large scale computational studies. The e-science solution presented here consists of (1) a methodology for comprehensive comparisons of contemporary documents containing text, images and vector graphics, (2) a framework for including 3D and 3D+time data sets into the appraisal analyses, (3) interfaces supporting exploratory archival appraisal analyses with small scale data sets, and (4) infrastructure supporting the transition from small scale to large scale computations using commodity and high performance computing resources. The novelty of our work is in designing methodologies, mathematical frameworks and prototypes for comprehensive and scalable document appraisals that include text, images, vector graphics, and high dimensional data.

This work presents a virtual observatory of the Abraham Lincoln writings. Many of the Lincoln correspondence papers and legal documents have been digitized, pre-processed and annotated for public viewing. However, there are challenges in delivering the information to the general public due to the large number of documents, very large volume of digital files, lack of metadata, heterogeneous pieces of information and need for services such as searching and transcription. We have prototyped an advanced information system that delivers Lincoln writings together with historical maps, contemporary Google maps, and Lincoln Log metadata, and other types of information to provide a multi-dimensional view of Lincoln's life. The novelty of our work is in designing the system that provides such a unique multi-dimensional view of Lincoln's life by combining technologies needed for building virtual observatories.

We have designed a framework for content based appraisal of documents. Our motivation is to provide computer assisted support for answering several appraisal criteria according to the general appraisal guidelines in the National Archives and Record Administration (NARA) 1441 directive. The appraisal criteria led us to investigations related to (a) finding groups of PDF documents with similar content, (b) ranking documents according to their creation/ modification time and digital volume, and (c) detecting inconsistency between ranking and content within a group of related documents. The novelty of our work is in designing a methodology and a mathematical framework for document appraisals, and prototyping the framework working with text, image and vector graphics components of PDF documents. We present example results of grouping, ranking and integrity verification for groups of scientific documents about medical topics.

This paper addresses the problem of understanding preservation and reconstruction requirements for computer- aided medical decision-making. With an increasing number of computer-aided decisions having a large impact on our society, the motivation for our work is not only to document these decision processes semi-automatically but also to understand the preservation cost and related computational requirements. Our objective is to support computer-assisted creation of medical records, to guarantee authenticity of records, as well as to allow managers of electronic medical records (EMR), archivists and other users to explore and evaluate computational costs (e.g., storage and processing time) depending on several key characteristics of appraised records. Our approach to this problem is based on designing an exploratory simulation framework for investigating preservation tradeoffs and assisting in appraisals of electronic records. We have a prototype simulation framework called image provenance to learn (IP2Learn) to support computer-aided medical decisions based on visual image inspection. The current software enables to explore some of the tradeoffs related to (1) information granularity (category and level of detail), (2) representation of provenance information, (3) compression, (4) encryption, (5) watermarking and steganography, (6) information gathering mechanism, and (7) final medical report content (level of detail) and its format. We illustrate the novelty of IP2Learn by performing example studies and the results of tradeoff analyses for a specific image inspection task.

In this paper, we address the problem of 3D volume reconstruction from depth adjacent subvolumes (i.e., sets of image frames) acquired using a confocal laser scanning microscope (CLSM). Our goal is to align sub-volumes by estimating an optimal global image transformation which preserves morphological smoothness of medical structures (called features, e.g., blood vessels) inside of a reconstructed 3D volume. We approached the problem by learning morphological characteristics of structures inside of each sub-volume, i.e. centroid trajectories of features. Next, adjacent sub-volumes are aligned by fusing the morphological characteristics of structures using extrapolation or model fitting. Finally, a global sub-volume to subvolume transformation is computed based on the entire set of fused structures. The trajectory-based 3D volume reconstruction method described here is evaluated with a pair of consecutive physical sections using two evaluation metrics for morphological continu

NCSA's Environmental Cyberinfrastructure Demonstration (ECID) project is developing a cyberenvironment to support environmental science and hydrology research. The ECID Cyberenvironment is based on a set of coordinating technologies that together provide unique capabilities for integrating local and remote work and for capturing and exploiting data and interaction provenance. The CyberCollaboratory portal provides collaboration tools to discover, share, analyze, and discuss data and information. The CyberIntegrator is a meta-workflow engine combining heterogeneous (local and remote) tools into workflows to support complex scientific analyses and simulations. Both of these tools record rich provenance into the RDF-based Tupelo 2 Toolkit where it can be graphically browsed or used through CI-KNOW, social network analysis software, to provide context-specific recommendations within the CyberCollaboratory and CyberIntegrator. These capabilities, and the ability to dynamically connect new tools into group spaces, workflows, and provenance trails, will be critical to the next-generation of community-scale, persistent cyberinfrastructure efforts.

We address the problem of 3D medical volume reconstruction using Web services. The use of proposed Web services is motivated by the fact that the problem of 3D medical volume reconstruction requires significant computer resources and human expertise in medical and computer science areas. Web services were implemented as an additional layer to a dataflow framework called data to knowledge. In the collaboration between UIC and NCSA, pre-processed input images at NCSA were made accessible to medical collaborators for registration. Every time medical collaborators inspected images and selected corresponding features for registration, the Web server at NCSA was contacted and the registration processing query was executed using the image to knowledge library of registration methods. Co-registered frames were returned for verification by medical collaborators in a new window. This paper presents 3D volume reconstruction problem requirements, architecture of the developed prototype system and the tradeoffs of our system design.

We propose a new methodology for fusing temporally changing multisensor raster and vector data by developing a spatially and temporally varying uncertainty model of acquired and transformed multisensor measurements. The proposed uncertainty model includes errors due to (1) each sensor by itself, e.g., sensor noise; (2) transformations of measured values to obtain comparable physical entities for data fusion and/or to calibrate sensor measurements; (3) vector data spatial interpolation that is needed to match different spatial resolutions of multisensor data; and (4) temporal interpolation that has to take place if multisensor acquisitions are not accurately synchronized. The proposed methodology was tested using simulated data with varying (a) amount of sensor noise, (b) spatial offset of point sensors generating vector data, and (c) model complexity of the underlying physical phenomenon. We demonstrated the multisensor fusion approach with a data set from a structural health monitoring application domain.

We present a new automatic grid alignment algorithm for detecting two-dimensional (2-D) arrays of spots in DNA microarray images. Our motivation for this work is the lack of automation in high-throughput microarray data analysis that leads to a) spatial inaccuracy of located spots and hence inaccuracy of extracted information from a spot and b) inconsistency of extracted features due to manual selection of grid alignment parameters. The proposed grid alignment algorithm is novel in the sense that 1) it can detect irregularly row- and column-spaced spots in a 2-D array, 2) it is independent of spot color and size, 3) it is general to localize a grid of other primitive shapes than the spot shapes, 4) it can perform grid alignment on any number of input channels, 5) it reduces the number of free parameters to minimum by data driven optimization of most algorithmic parameters, and 6) it has a built-in speed versus accuracy tradeoff mechanism to accommodate user's requirements on performance time and accuracy of the results. The developed algorithm also automatically identifies multiple blocks of 2-D arrays, as it is the case in microarray images, and compensates for grid rotations in addition to grid translations.

This paper presents a new approach to hierarchical clustering of point patterns. Two algorithms for hierarchical location- and density-based clustering are developed. Each method groups points such that maximum intracluster similarity and intercluster dissimilarity are achieved for point locations or point separations. Performance of the clustering methods is compared with four other methods. The approach is applied to a two-step texture analysis, where points represent centroid and average color of the regions in image segmentation.

This paper presents a clustering algorithm for dot patterns in n-dimensional space. The n-dimensional space often represents a multivariate (n/sub f/-dimensional) function in a n/sub s/-dimensional space (n/sub s/+n/sub f/=n). The proposed algorithm decomposes the clustering problem into the two lower dimensional problems. Clustering in n/sub f/-dimensional space is performed to detect the sets of dots in n-dimensional space having similar n/sub f/-variate function values (location based clustering using a homogeneity model). Clustering in n/sub s/ dimensional space is performed to detect the sets of dots in n-dimensional space having similar interneighbor distances (density based clustering with a uniformity model). Clusters in the n-dimensional space are obtained by combining the results in the two subspaces.