...1. Introduction Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). Cluster analysis is an unsupervised form of learning, which means, that it doesn't use class labels. This is different from methods like discriminant analysis which use class labels and come under the category of supervised learning. K-means is the most simple and popular algorithm in clustering and was published in 1955, 50 years ago. The advancement in technology has led to many high-volume, high-dimensional data sets. These huge data sets provide opportunity for automatic data analysis, classification...
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...in the Data mining course. It includes my two proposed approaches in the field of clustering, my learn lessons in class and my comment on this class. The report’s outline is as following: Part I: Proposed approaches 1. Introduction and backgrounds 2. Related works and motivation 3. Proposed approaches 4. Evaluation method 5. Conclusion Part II: Lessons learned 1. Data preprocessing 2. Frequent pattern and association rule 3. Classification and prediction 4. Clustering Part III: My own comments on this class. I. Proposed approach • An incremental subspace-based K-means clustering method for high dimensional data • Subspace based document clustering and its application in data preprocessing in Web mining 1. Introduction and background High dimensional data clustering has many applications in real world, especially in bioinformatics. Many well-known clustering algorithms often use a whole-space distance score to measure the similarity or distance between two objects, such as Euclidean distance, Cosine function... However, in fact, when the dimensionality of space or the number of objects is large, such whole-space-based pairwise similarity scores are no longer meaningful, due to the distance of each pair of object nearly the same [5]. Pattern-space clustering, a kind of subspace clustering, can overcome above problem by discovering such patterns that existing in subspaces...
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...subjects–that is, the resemblance of their profiles over the whole set of variables. These variables may be the original set or may consist of a representation of them in reduced space (i.e., factor scores). In either case the objective of cluster analysis is to find similar groups of subjects, where “similarity” between each pair of subjects is usually construed to mean some global measure over the whole set of characteristics–either original variables or derived coordinates, if preceded by a reduced space analysis. In this section we discuss various methods of clustering and the key role that distance functions play as measures of the proximity of pairs of points. We first discuss the fundamentals of cluster analysis in terms of major questions concerning choice of proximity measure, choice of clustering technique, and descriptive measures by which the resultant clusters can be defined. We show that clustering results can be sensitive to the type of distance function used to summarize proximity between pairs of profiles. We next discuss the characteristics of various computational algorithms that are used for grouping profiles, i.e., for partitioning the rows (subjects) of the data matrix. This is followed by brief discussions of statistics for defining...
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...eurojournals.com/ajsr.htm A Survey of Clustering Schemes for Mobile Ad-Hoc Network (MANET) Ismail Ghazi Shayeb Albalqa Applied Univesity, Amman, Jordan E-mail: ismail@bau.edu.jo AbdelRahman Hamza Hussein Jearsh University, Jearsh, Jordan E-mail: Abed_90@yahoo.com Ayman Bassam Nasoura Jearsh University, Jearsh, Jordan E-mail: nassuora@yahoo.com Abstract Clustering has been found to be an effective means of resource management for MANETs regarding network performance, routing protocol design, Quality of Service (QoS) and network modeling though it has yet to be refined to satisfy all the issues that might be faced by choosing this approach. Scalability is of particular interest to ad hoc network designers and users and is an issue with critical influence on capability and capacity. Where topologies include large numbers of nodes, routing packets will demand a large percentage of the limited wireless bandwidth and this is exaggerated and exacerbated by the mobility feature often resulting in a high frequency of failure regarding wireless links. In this paper we present acomprehensive survey and classification of recently published clustering algorithm, which we classify based on their objectives. We survey different clustering algoirthm for MANET's; highlighting the defining clustering, the design goals of clustering algorithms, advantages of clustering for ad hoc networks, challenges facing clustering including cost issues and classifying clustering algorithms as well as discussion...
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...4/18/2004 2 Why Mine Data? Scientific Viewpoint Data collected and stored at enormous speeds (GB/hour) – remote sensors on a satellite – telescopes scanning the skies – microarrays generating gene expression data – scientific simulations generating terabytes of data Traditional techniques infeasible for raw data Data mining may help scientists – in classifying and segmenting data – in Hypothesis Formation Mining Large Data Sets - Motivation There is often information “hidden” in the data that is not readily evident Human analysts may take weeks to discover useful information Much of the data is never analyzed at all 4,000,000 3,500,000 3,000,000 2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 1995 1996 1997 The Data Gap Total new disk (TB) since 1995 Number of analysts 1998 1999 4 © Tan,Steinbach, KumarKamath, V. Kumar, “Data Mining for Mining and Engineering Applications” From: R. Grossman, C. Introduction to Data Scientific 4/18/2004 What is Data Mining? Many Definitions – Non-trivial extraction of implicit, previously unknown and potentially useful information from data – Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns © Tan,Steinbach, Kumar Introduction to Data...
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...Technological advances have led to new and automated data collection methods. Datasets once at a premium are often plentiful nowadays and sometimes indeed massive. A new breed of challenges are thus presented – primary among them is the need for methodology to analyze such masses of data with a view to understanding complex phenomena and relationships. Such capability is provided by data mining which combines core statistical techniques with those from machine intelligence. This article reviews the current state of the discipline from a statistician’s perspective, illustrates issues with real-life examples, discusses the connections with statistics, the differences, the failings and the challenges ahead. 1 Introduction The information age has been matched by an explosion of data. This surfeit has been a result of modern, improved and, in many cases, automated methods for both data collection and storage. For instance, many stores tag their items with a product-specific bar code, which is scanned in when the corresponding item is bought. This automatically creates a gigantic repository of information on products and product combinations sold. Similar databases are also created by automated book-keeping, digital communication tools or by remote sensing satellites, and aided by the availability of affordable and effective storage mechanisms – magnetic tapes, data warehouses and so on. This has created a situation of plentiful data and the potential for new and deeper understanding of...
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...fuzzy clustering approach. Abstract Purpose segmentation is the point where marketing activity starts. A flawless segmentation results in comparable competitive advantage. The purpose of this study is to examine the stability of segmentation. Design / methodology/ approach - this research examines the stability of the segments. Shoppers have been segmented based on the importance they’ve given to store image. Data collected through mall intercept interviews has been used for it. Segmentation has been done by K-means clustering and fuzzy clustering methods. Membership grades give the samples’ relative position in the cluster. Findings – Various approaches to segment the market has been analysed and the advantages of fuzzy methods has been obtained. Finally the most stable segment, on the other hand the most volatile segment has been found out. Study reveals that fuzzy clustering is potentially useful to assess the stability of segments. Research limitations / implications Research findings are constrained, as the study concentrates on the behaviour of shoppers based on the influence of store images but segmenting based on demographic or lifestyle variables are not considered. However the stability of segments has been analysed for this segments. Practical implications membership grade gives a clear picture of the real market to the marketer. And it helps the marketer to visualize individual’s level of multiple preferences. Hence the marketer can develop new strategies...
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...DARE is an adaptive data replication mechanism that helps in achieving a high degree of data locality. DARE[6] adapts to the change in workload conditions. Each node executes the algorithm to create replicas independently of heavily accessed files in a short interval of time. Data with correlated access are distributed to various nodes as new replicas are created and old ones expire which also enhance data locality. No extra network overhead is incurred since it is making use of existing remote data retrieval. The algorithm creates replicas of popular files and at the same time minimizes the number of replicas of unpopular...
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............................................................ 11 8. REFERENCES......................................................................................................... 13 9. APPENDIX ............................................................................................................ 14 The Learning Business Research Report | Campus: 10. 1 CHECKLIST OF ASSESSMENT CRITERIA .................................................................. 18 The Learning Business Research Report 1. Executive Summary This report discusses the rationale behind strategic decision taken by Royal bank of Scotland (RBS) in moving its headquarters from Glasglow to Leeds, Yorkshire. The benefits of financial clustering in the UK have been analyzed and assessed on the basis of theoretical evidences collected from literature review and qualitative data collected. There is an emphasis on financial information...
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...manipulates by computer directly and can be analysis on a screen and also we can stored that and get as printed form. That technology provide lot of benefits such as: The procedure uses less radiation than the typical X-ray and there is no wait time for the X-rays to develop. The images are available on a screen a few seconds after being taken. The image taken, of a tooth for example, can be enhanced and enlarged many times its actual size on the computer screen, making it easier for your dentist to show you where and what the problem is.If necessary, images can be electronically sent to another dentist or specialist – for instance, for a second opinion on a dental problem to determine if a specialist is needed. The images can also be sent to a new dentist (for example, if you move).Software added to the computer can help dentists digitally compare current to previous images in a process called subtraction radiography. Using this technique, everything that is the same between two images is "subtracted out" from the image, leaving a clear image of only the portion that is different. This helps dentists easily see the tiniest changes that might not have been noticed by the naked eye. 2. Image Processing: image processing has become a vast domain of modern signal technologies. Its applications pass far beyond simple aesthetical considerations, and they include medical descriptions, television and multimedia signals, security, moveable digital devices, video compression, and even digital...
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...Management 33 (2004) 607 – 617 Complementary approaches to preliminary foreign market opportunity assessment: Country clustering and country ranking S. Tamer Cavusgil*, Tunga Kiyak, Sengun Yeniyurt Department of Marketing and Supply Chain Management, The Eli Broad Graduate School of Management, Michigan State University, 370 North Business College, East Lansing, MI 48824, USA Received 2 November 1998; received in revised form 16 May 2003; accepted 23 October2003 Available online 24 December 2003 Abstract Companies seeking to expand abroad are faced with the complex task of screening and evaluating foreign markets. How can managers define, characterize, and express foreign market opportunity? What makes a good market, an attractive industry environment? National markets differ in terms of market attractiveness, due to variations in the economic and commercial environment, growth rates, political stability, consumption capacity, receptiveness to foreign products, and other factors. This research proposes and illustrates the use of two complementary approaches to preliminary foreign market assessment and selection: country clustering and country ranking. These two methods, in combination, can be extremely useful to managerial decision makers in the early stages of foreign market selection. D 2004 Published by Elsevier Inc. Keywords: Country ranking; Clustering; Foreign market selection; Country market assessment 1. Introduction Marketing across national boundaries has become...
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...1 Video Data Mining JungHwan Oh University of Texas at Arlington, USA JeongKyu Lee University of Texas at Arlington, USA Sae Hwang University of Texas at Arlington, USA 8 INTRODUCTION Data mining, which is defined as the process of extracting previously unknown knowledge and detecting interesting patterns from a massive set of data, has been an active research area. As a result, several commercial products and research prototypes are available nowadays. However, most of these studies have focused on corporate data — typically in an alpha-numeric database, and relatively less work has been pursued for the mining of multimedia data (Zaïane, Han, & Zhu, 2000). Digital multimedia differs from previous forms of combined media in that the bits representing texts, images, audios, and videos can be treated as data by computer programs (Simoff, Djeraba, & Zaïane, 2002). One facet of these diverse data in terms of underlying models and formats is that they are synchronized and integrated hence, can be treated as integrated data records. The collection of such integral data records constitutes a multimedia data set. The challenge of extracting meaningful patterns from such data sets has lead to research and development in the area of multimedia data mining. This is a challenging field due to the non-structured nature of multimedia data. Such ubiquitous data is required in many applications such as financial, medical, advertising and Command, Control, Communications and Intelligence...
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...HadoopJitter: The Ghost in the Cloud and How to Tame It Vivek Kale∗ , Jayanta Mukherjee† , Indranil Gupta‡ , William Gropp§ Department of Computer Science, University of Illinois at Urbana-Champaign 201 North Goodwin Avenue, Urbana, IL 61801-2302, USA Email: ∗ vivek@illinois.edu, † mukherj4@illinois.edu, ‡ indy@illinois.edu, § wgropp@illinois.edu Abstract—The small performance variation within each node of a cloud computing infrastructure (i.e. cloud) can be a fundamental impediment to scalability of a high-performance application. This performance variation (referred to as jitter) particularly impacts overall performance of scientific workloads running on a cloud. Studies show that the primary source of performance variations comes from disk I/O and the underlying communication network [1]. In this paper, we explore the opportunities to improve performance of high performance applications running on emerging cloud platforms. Our contributions are 1. the quantification and assessment of performance variation of data-intensive scientific workloads on a small set of homogeneous nodes running Hadoop and 2. the development of an improved Hadoop scheduler that can improve performance (and potentially scalability) of these application by leveraging the intrinsic performance variation of the system. In using our enhanced scheduler for data-intensive scientific workloads, we are able to obtain more than a 21% performance gain over the default Hadoop scheduler. I. I NTRODUCTION Certain...
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...Correlation Based Dynamic Clustering and Hash Based Retrieval for Large Datasets ABSTRACT Automated information retrieval systems are used to reduce the overload of document retrieval. There is a need to provide an efficient method for storage and retrieval .This project proposes the use of dynamic clustering mechanism for organizing and storing the dataset according to concept based clustering. Also hashing technique will be used to retrieve the data from the dataset based on the association rules .Related documents are grouped into same cluster by k-means clustering algorithm. From each cluster important sentences are extracted by concept matching and also based on sentence feature score. Experiments are carried to analyze the performance of the proposed work with the existing techniques considering scientific articles and news tracks as data set .From the analysis it is inferred that our proposed technique gives better enhancement for the documents related to scientific terms. Keywords Document clustering, concept extraction, K-means algorithm, hash-based indexing, performance evaluation 1. INTRODUCTION Now-a-days online submission of documents has increased widely, which means large amount of documents are accumulated for a particular domain dynamically. Information retrieval [1] is the process of searching information within the documents. An information retrieval process begins when a user enters a query; queries are formal statements of...
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...This approach is based on spatial correlation of pixel therefore segmented result is better than thresholding. Regions are splitted which do not satisfy a given homogeneity criteria. Splitting and merging can be used together and its performance depends on the selected homogeneity criterion. The main drawback is seed is manually selected. Region growing can be so sensitive to the noises that it may cause extracted region to have holes or even is disconnected. Region growing is not often used because it is not sufficient to segment brain structure accurately and robustly. As compared to edge detection methods, region growing methods are simpler and have strongly immune to noise...
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