data mining cluster analysis example
Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining pot
... Introduction to Data Mining Notion of a Cluster can be Ambiguous How many clusters? Six Clusters Two Clusters Four Clusters © Tan,Steinbach, Kumar Introduction to Data Mining Types of Clusterings A clustering ... Introduction to Data Mining 18 Clustering Algorithms K-means and its variants Hierarchical clustering Density-based clustering © Tan,Steinbach, Kumar Introduction to Data Mining 19 K-means Clustering ... point of a cluster center-based clusters © Tan,Steinbach, Kumar Introduction to Data Mining 12 Types of Clusters: Contiguity-Based Contiguous Cluster (Nearest neighbor or Transitive) – A cluster...
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Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining pot
... Density Introduction to Data Mining 33 SNN Clustering Can Handle Differing Densities Original Points © Tan,Steinbach, Kumar SNN Clustering Introduction to Data Mining 34 SNN Clustering Can Handle ... Kumar Introduction to Data Mining 35 Finding Clusters of Time Series In Spatio-Temporal Data SNN Density of SLP Time Series Data 26 SLP Clusters via Shared Nearest Neighbor Clustering (100 NN, ... Data Mining 10 Sparsification in the Clustering Process © Tan,Steinbach, Kumar Introduction to Data Mining 11 Limitations of Current Merging Schemes Existing merging schemes in hierarchical clustering...
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Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining pdf
... Introduction to Data Mining 34 Alternative Methods for Frequent Itemset Generation Representation of Database – horizontal vs vertical data layout © Tan,Steinbach, Kumar Introduction to Data Mining 35 ... to Data Mining D=>ABC 48 Effect of Support Distribution Many real data sets have skewed support distribution Support distribution of a retail data set © Tan,Steinbach, Kumar Introduction to Data ... Tan,Steinbach, Kumar Introduction to Data Mining 12 Illustrating Apriori Principle Found to be Infrequent Pruned supersets © Tan,Steinbach, Kumar Introduction to Data Mining 13 Illustrating Apriori...
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analysis services data mining _ môn data mining
... (Intermediate Data Mining Tutorial) See Also Data Mining Tutorial Intermediate Data Mining Tutorial (Analysis Services - Data Mining) Microsoft Time Series Algorithm (Analysis Services - Data Mining) ... Data Mining Algorithms (Analysis Services - Data Mining) Data Mining Extensions (DMX) Reference Related Sections Using the Data Mining Tools Logical Architecture (Analysis Services - Data Mining) ... Creating and Querying Data Mining Models with DMX: Tutorials (Analysis Services - Data Mining) Basic Data Mining Tutorial Welcome to the Microsoft Analysis Services Basic Data Mining Tutorial Microsoft...
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báo cáo sinh học:" Workforce analysis using data mining and linear regression to understand HIV/AIDS prevalence patterns" pdf
... obtained from the WHO/UNAIDS database [9] The data from the various data sources were merged into one file at the country level for analysis The variables in the data set included the following ... to the WHO Authors' contributions MZ developed the merged data set OLC performed the data mining EAM performed the multiple regression analysis The generation of the idea and writing of the paper ... for prediction: as an example, CART can help predict levels of HIV/AIDS prevalence rates based on previously learnt data CART can also be used for interpretative purposes For example, it can be...
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Báo cáo hóa học: " Research Article The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data" pptx
... transformed data can be further analyzed by cluster analysis We demonstrate this approach with temporal expression profiles for a single gene under 72 growth conditions Clustering of the data after ... principal components analysis and machine learning Application of clustering analysis directly to the expression data ignores some basic features of temporal expression data and more over can ... disperse The wavelet analysis is able to overcome the profile shift problem, meanwhile, it is worth noting that the analysis loses time series information 3.3 Clustering analysis and evaluation...
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Báo cáo y học: " Cluster analysis in severe emphysema subjects using phenotype and genotype data: an exploratory investigation" doc
... factor analysis as a guide to determine which COPD phenotypic variables to include in our clustering analysis[ 31] Factor analysis is a data reduction technique related to principal component analysis, ... NL assisted in the statistical analysis GJC, EAH, and FJM participated in generating the data and in data analysis JJR helped design the study and assisted in data analysis All authors read, helped ... MHC carried out the data analysis and drafted the manuscript EKS conceived and designed the study, and assisted in data analysis and interpretation GRW and EAH generated the CT data TH and NL assisted...
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Data mining methodologies for gene expression analysis application to strain improvement
... expression data necessitates use of data- mining techniques to organize and extract useful information from these data Clustering is one such technique widely used for gene expression data analysis ... k-means clustering for clustering yeast Saccharomyces cerevisiae cell-cycle data and identified novel TFs 17 2.2.3 Model-based clustering Model-based clustering approach assumes that the data to be clustered ... identifies clusters Dunn’s (dot line) predicts clusters Davies-Bouldin (dash-dot line) predicts clusters 130 6.15 Results for Pancreas dataset NIFTI (solid line) finds clusters in this dataset...
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Data mining techniques in gene expression data analysis
... association rule mining and classification while unsupervised data mining methods mainly refer to the various clustering methods Class association rule mining is one well-known data mining task Each ... high-dimensional databases besides gene expression data Experiments on synthetic data, gene expression data and benchmark biological data are done to show the effectiveness of our method Reg -Cluster: ... for the Image Dataset101 4.15 NNCO Plot of Iyer 105 xiii 4.16 Discovered Subclusters for Cluster “D” 105 4.17 Discovered Subclusters for Cluster “H” ...
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Data warehuose and data mining
... Evaluation Data mining Task relevant data Data warehouse Data cleaning Knowledge Data integration selection Mục đích KTDL Data Mining Descriptive Predictive Classification Time series analysis ... Environment • Subject = Customer • Data Warehouse Biến thời gian • Time • Data • 01/97 Data for January • • 02/97 Data for February • • 03/97 Data for March • • Data • Warehouse Ổn Định • Là lưu ... Nội Dung • Kho liệu (Data warehouse) • Khai thác liệu (Data mining) – Giới thiệu – Giới thiệu – Qui trình khám phá tri thức – Định nghĩa – DW - Traditional Database – Luật kết hợp – Mục...
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Data Mining - Chapter 2
... lý liệu Pattern Evaluation/ Presentation Data Mining Patterns Task-relevant Data Data Warehouse Data Cleaning Selection/Transformation Data Integration Data Sources 2.1 Tổng quan giai đoạn tiền ... ZhaoHui Tang, Jamie MacLennan, Data Mining with SQL Server 2005”, Wiley Publishing, 2005 [6] Oracle, Data Mining Concepts”, B28129-01, 2008 [7] Oracle, Data Mining Application Developer’s ... Micheline Kamber, Data Mining: Concepts and Techniques”, Second Edition, Morgan Kaufmann Publishers, 2006 [2] David Hand, Heikki Mannila, Padhraic Smyth, “Principles of Data Mining , MIT Press,...
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Data mining
... quan sát (hồ sơ) đến trung tâm cụm Show cluster proximity: Khoảng cách trung tâm cụm Cluster label : Tên thành viên cụm, String kiểu chuỗi (ví dụ "Cluster1 ", "cluster2 ", vv), number số 1,2 Lưu ý ... tên lại cho lệnh “phan cum” hay tùy ý bạn Use partitioned data: Sử dụng liệu phân vùng Nếu trước liệu bạn thực lệnh Partition Number of clusters: Xác định số lượng cụm để tạo (Mặc định 5), Ở chọn ... hóa): Các nút sử dụng mô hình hóa thuật toán có sẵn Clementine, mạng thần kinh, định, thuật toán clustering, xếp liệu • Output: Các nút xuất loạt liệu, bảng biểu, kết mô hình, xem Clementine gửi...
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Data Mining Tutorial
... small dataset, need all observations to estimate parameters of interest • Data mining – loads of data, can afford “holdout sample” • Variation: n-fold cross validation – Randomly divide data into ... April 2012 Data Mining - What is it? • • • • Large datasets Fast methods Not significance testing Topics – Trees (recursive splitting) – Logistic Regression – Neural Networks – Association Analysis ... Multiple testing • • • • • • 50 different BPs in data, m=49 ways to split Multiply p-value by 49 Bonferroni – original idea Kass – apply to data mining (trees) Stop splitting if minimum p-value...
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data-mining-tutorial
... Many Names of Data Mining Data Fishing, Data Dredging: 1960 used by statisticians (as bad name) Data Mining :1990 - used in DB community, business Knowledge Discovery in Databases (1989-) ... Outline Introduction Data Mining Tasks Classification & Evaluation Clustering Application Examples © 2006 KDnuggets Trends leading to Data Flood More data is generated: Web, text, ... training data, validation data, and test data Validation data is used to optimize parameters © 2006 KDnuggets 45 Making the most of the data Once evaluation is complete, all the data can...
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hash-based approach to data mining
... Hash-Based Approach to Data Mining Hk.prune(minsup); k++; until Lk-1 = ∅; Answer = ∪k Lk ; 2.2.3 Example Example 3: (similar to example 2, using PHP algorithm) Figure 2: Example of hash table ... k++; end Answer = decode (LUTk); 2.3.3 Example Example 4: same as in example 2, work with the PHS algorithm Transaction database 25 Hash-Based Approach to Data Mining TID Items 100 ABCD 200 ABCDF ... candidates c ∈ Ct Hash-Based Approach to Data Mining c.count++; end Lk = {c ∈ Ck | c.count >= minsup} end Answer = ∪k Lk; Example: Example 1: Consider the database in table and assume that the minsup...
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Data mining and medical knowledge management cases and applications
... drive data gathering and experimental planning, and to structure the databases and data warehouses BK is used to properly select the data, choose the data mining strategies, improve the data mining ... modern data mining methods in several important areas of medicine, covering classical data mining methods, elaborated approaches related to mining in EEG and ECG data, and methods related to mining ... handling data receives, we can say that a new field is being born, called data engineering One of the essential notions of data engineering is metadata It is data about data , i.e., a data description...
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CUSTOMER SATISFACTION USING DATA MINING TECHNIQUES
... BASED DATA MINING TECHNIQUES The objective of data mining is to extract valuable information from one’s data, to discover the ‘hidden gold’ In Decision Support Management terminology, data mining ... Clusters MUSA Satisfaction Functions Data Mining Search Engines Statistical Analysis Rule Induction Engine Filling the empty cells Patterns / Rules MUSA Global Satisfaction Predicction Data Mining ... on data retention and data distillation Rule induction models (Figure 2) belong to the logical, pattern distillation based approaches of data mining These technologies extract patterns from data...
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Data Preparation for Data Mining- P3
... of data representation 2.6.2 Building Data Dealing with Variables The data representation can usefully be looked at from two perspectives: as data and as a data set The terms data and data ... actual mining due to their limited data capacity and inability to handle certain types of operations needed in data preparation, data surveying, and data modeling For exploring small data sets, ... information is crucial to data mining It is the very substance enfolded within a data set for which the data set is being mined It is the reason to prepare the data set for mining to best expose...
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Data Preparation for Data Mining- P4
... bias Determining data structure Building the PIE Surveying the data Modeling the data 3.3.1 Stage 1: Accessing the Data The starting point for any data preparation project is to locate the data This ... data preparation requires three such steps: data discovery, data characterization, and data set assembly • Data discovery consists of discovering and actually locating the data to be used • Data ... preparation activities Data Issue: Representative Samples A perennial problem is determining how much data is needed for modeling One tenet of data mining is “all of the data, all of the time.”...
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Data Preparation for Data Mining- P5
... responders is an example of enhancing the data No external data is added, but the existing data is restructured to be more useful in a particular situation Another form of data enhancement is data multiplication ... additional information actually forms another data stream and enriches the original data Enrichment is the process of adding external data to the data set Note that data enhancement is sometimes confused ... understand the data Once the assay is completed, the mining data set, or sets, can be assembled Given assembled data sets, much preparatory work still remains to be done before the data is in optimum...
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