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The document summarizes the CURE clustering algorithm, which uses a hierarchical approach that selects a constant number of representative points from each cluster to address limitations of centroid-based and all-points clustering methods. It employs random sampling and partitioning to speed up processing of large datasets. Experimental results show CURE detects non-spherical and variably-sized clusters better than compared methods, and it has faster execution times on large databases due to its sampling approach.

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05 Clustering in Data Mining

Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.

Clustering in Data Mining

Clustering is an unsupervised learning technique used to group unlabeled data points together based on similarities. It aims to maximize similarity within clusters and minimize similarity between clusters. There are several clustering methods including partitioning, hierarchical, density-based, grid-based, and model-based. Clustering has many applications such as pattern recognition, image processing, market research, and bioinformatics. It is useful for extracting hidden patterns from large, complex datasets.

hierarchical methods

This document discusses different hierarchical clustering methods. It describes agglomerative and divisive hierarchical clustering approaches and compares their bottom-up and top-down strategies. It also discusses distance measures used in hierarchical clustering algorithms and introduces specific hierarchical clustering algorithms like BIRCH, Chameleon, and probabilistic hierarchical clustering.

3.5 model based clustering

The document discusses various model-based clustering techniques for handling high-dimensional data, including expectation-maximization, conceptual clustering using COBWEB, self-organizing maps, subspace clustering with CLIQUE and PROCLUS, and frequent pattern-based clustering. It provides details on the methodology and assumptions of each technique.

Machine learning with ADA Boost

This document discusses machine learning and artificial intelligence. It defines machine learning as a branch of AI that allows systems to learn from data and experience. Machine learning is important because some tasks are difficult to define with rules but can be learned from examples, and relationships in large datasets can be uncovered. The document then discusses areas where machine learning is influential like statistics, brain modeling, and more. It provides an example of designing a machine learning system to play checkers. Finally, it discusses machine learning algorithm types and provides details on the AdaBoost algorithm.

Data Mining: Concepts and techniques: Chapter 13 trend

Mining Complex Types of Data,
Other Methodologies of Data Mining,
Data Mining Applications,
Data Mining and Society,
Data Mining Trends,
Summary
by
Jiawei Han, Micheline Kamber, and Jian Pei,
University of Illinois at Urbana-Champaign &
Simon Fraser University,
©2013 Han, Kamber & Pei. All rights reserved.

1.7 data reduction

The document discusses various data reduction strategies including attribute subset selection, numerosity reduction, and dimensionality reduction. Attribute subset selection aims to select a minimal set of important attributes. Numerosity reduction techniques like regression, log-linear models, histograms, clustering, and sampling can reduce data volume by finding alternative representations like model parameters or cluster centroids. Dimensionality reduction techniques include discrete wavelet transformation and principal component analysis, which transform high-dimensional data into a lower-dimensional representation.

Data Mining Concepts and Techniques, Chapter 10. Cluster Analysis: Basic Conc...

slides contain:
Cluster Analysis: Basic Concepts
Partitioning Methods
Hierarchical Methods
Density-Based Methods
Grid-Based Methods
Evaluation of Clustering
Summary
by
Jiawei Han, Micheline Kamber, and Jian Pei,
University of Illinois at Urbana-Champaign &
Simon Fraser University,
©2013 Han, Kamber & Pei. All rights reserved.

05 Clustering in Data Mining

Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.

Clustering in Data Mining

Clustering is an unsupervised learning technique used to group unlabeled data points together based on similarities. It aims to maximize similarity within clusters and minimize similarity between clusters. There are several clustering methods including partitioning, hierarchical, density-based, grid-based, and model-based. Clustering has many applications such as pattern recognition, image processing, market research, and bioinformatics. It is useful for extracting hidden patterns from large, complex datasets.

hierarchical methods

This document discusses different hierarchical clustering methods. It describes agglomerative and divisive hierarchical clustering approaches and compares their bottom-up and top-down strategies. It also discusses distance measures used in hierarchical clustering algorithms and introduces specific hierarchical clustering algorithms like BIRCH, Chameleon, and probabilistic hierarchical clustering.

3.5 model based clustering

The document discusses various model-based clustering techniques for handling high-dimensional data, including expectation-maximization, conceptual clustering using COBWEB, self-organizing maps, subspace clustering with CLIQUE and PROCLUS, and frequent pattern-based clustering. It provides details on the methodology and assumptions of each technique.

Machine learning with ADA Boost

This document discusses machine learning and artificial intelligence. It defines machine learning as a branch of AI that allows systems to learn from data and experience. Machine learning is important because some tasks are difficult to define with rules but can be learned from examples, and relationships in large datasets can be uncovered. The document then discusses areas where machine learning is influential like statistics, brain modeling, and more. It provides an example of designing a machine learning system to play checkers. Finally, it discusses machine learning algorithm types and provides details on the AdaBoost algorithm.

Data Mining: Concepts and techniques: Chapter 13 trend

Mining Complex Types of Data,
Other Methodologies of Data Mining,
Data Mining Applications,
Data Mining and Society,
Data Mining Trends,
Summary
by
Jiawei Han, Micheline Kamber, and Jian Pei,
University of Illinois at Urbana-Champaign &
Simon Fraser University,
©2013 Han, Kamber & Pei. All rights reserved.

1.7 data reduction

The document discusses various data reduction strategies including attribute subset selection, numerosity reduction, and dimensionality reduction. Attribute subset selection aims to select a minimal set of important attributes. Numerosity reduction techniques like regression, log-linear models, histograms, clustering, and sampling can reduce data volume by finding alternative representations like model parameters or cluster centroids. Dimensionality reduction techniques include discrete wavelet transformation and principal component analysis, which transform high-dimensional data into a lower-dimensional representation.

Data Mining Concepts and Techniques, Chapter 10. Cluster Analysis: Basic Conc...

slides contain:
Cluster Analysis: Basic Concepts
Partitioning Methods
Hierarchical Methods
Density-Based Methods
Grid-Based Methods
Evaluation of Clustering
Summary
by
Jiawei Han, Micheline Kamber, and Jian Pei,
University of Illinois at Urbana-Champaign &
Simon Fraser University,
©2013 Han, Kamber & Pei. All rights reserved.

3. mining frequent patterns

The document discusses frequent pattern mining and the Apriori algorithm. It introduces frequent patterns as frequently occurring sets of items in transaction data. The Apriori algorithm is described as a seminal method for mining frequent itemsets via multiple passes over the data, generating candidate itemsets and pruning those that are not frequent. Challenges with Apriori include multiple database scans and large number of candidate sets generated.

Dynamic Itemset Counting

Dynamic Itemset Counting (DIC) is an algorithm for efficiently mining frequent itemsets from transactional data that improves upon the Apriori algorithm. DIC allows itemsets to begin being counted as soon as it is suspected they may be frequent, rather than waiting until the end of each pass like Apriori. DIC uses different markings like solid/dashed boxes and circles to track the counting status of itemsets. It can generate frequent itemsets and association rules using conviction in fewer passes over the data compared to Apriori.

Clustering, k-means clustering

The document discusses K-means clustering, an unsupervised machine learning algorithm that partitions observations into k clusters defined by centroids. It compares clustering to classification, noting clustering does not use training data and maps observations into natural groupings. The K-means algorithm is then explained, with the steps of initializing centroids, assigning observations to the closest centroid, revising centroids as cluster means, and repeating until convergence. Applications of clustering in business contexts like banking, retail, and insurance are also briefly mentioned.

K means clustering

K-means clustering is an algorithm that groups data points into k clusters based on their attributes and distances from initial cluster center points. It works by first randomly selecting k data points as initial centroids, then assigning all other points to the closest centroid and recalculating the centroids. This process repeats until the centroids are stable or a maximum number of iterations is reached. K-means clustering is widely used for machine learning applications like image segmentation and speech recognition due to its efficiency, but it is sensitive to initialization and assumes spherical clusters of similar size and density.

Instance Based Learning in Machine Learning

Slides were formed by referring to the text Machine Learning by Tom M Mitchelle (Mc Graw Hill, Indian Edition) and by referring to Video tutorials on NPTEL

Clustering in data Mining (Data Mining)

It is a data mining technique used to place the data elements into their related groups. Clustering is the process of partitioning the data (or objects) into the same class, The data in one class is more similar to each other than to those in other cluster.

3.4 density and grid methods

The document discusses several density-based and grid-based clustering algorithms. DBSCAN is described as a density-based method that forms clusters as maximal sets of density-connected points. OPTICS extends DBSCAN to produce a special ordering of the database with respect to density-based clustering structure. DENCLUE uses density functions to allow mathematically describing arbitrarily shaped clusters. Grid-based methods like STING, WaveCluster, and CLIQUE partition space into a grid structure to perform fast clustering.

3.7 outlier analysis

Outlier analysis identifies outliers, which are data objects that are grossly different from or inconsistent with the remaining set of data. Outliers can be identified using statistical, distance-based, density-based, or deviation-based approaches. Statistical approaches assume an underlying data distribution and identify outliers based on significance probabilities. Distance-based approaches identify outliers as objects with too few neighbors within a given distance. Density-based approaches identify local outliers based on local density comparisons. Deviation-based approaches identify outliers as objects that deviate from the main characteristics of their data group.

Mining Frequent Patterns, Association and Correlations

This document summarizes Chapter 6 of the book "Data Mining: Concepts and Techniques" which discusses frequent pattern mining. It introduces basic concepts like frequent itemsets and association rules. It then describes several scalable algorithms for mining frequent itemsets, including Apriori, FP-Growth, and ECLAT. It also discusses optimizations to Apriori like partitioning the database and techniques to reduce the number of candidates and database scans.

Clock synchronization in distributed system

This document discusses several techniques for clock synchronization in distributed systems:
1. Time stamping events and messages with logical clocks to determine partial ordering without a global clock. Logical clocks assign monotonically increasing sequence numbers.
2. Clock synchronization algorithms like NTP that regularly adjust system clocks across the network to synchronize with a time server. NTP uses averaging to account for network delays.
3. Lamport's logical clocks algorithm that defines "happened before" relations and increments clocks between events to synchronize logical clocks across processes.

Clustering: Large Databases in data mining

The document discusses different approaches for clustering large databases, including divide-and-conquer, incremental, and parallel clustering. It describes three major scalable clustering algorithms: BIRCH, which incrementally clusters incoming records and organizes clusters in a tree structure; CURE, which uses a divide-and-conquer approach to partition data and cluster subsets independently; and DBSCAN, a density-based algorithm that groups together densely populated areas of points.

01 Data Mining: Concepts and Techniques, 2nd ed.

The document provides an overview of data mining concepts and techniques. It introduces data mining, describing it as the process of discovering interesting patterns or knowledge from large amounts of data. It discusses why data mining is necessary due to the explosive growth of data and how it relates to other fields like machine learning, statistics, and database technology. Additionally, it covers different types of data that can be mined, functionalities of data mining like classification and prediction, and classifications of data mining systems.

Types of clustering and different types of clustering algorithms

The document discusses different types of clustering algorithms:
1. Hard clustering assigns each data point to one cluster, while soft clustering allows points to belong to multiple clusters.
2. Hierarchical clustering builds clusters hierarchically in a top-down or bottom-up approach, while flat clustering does not have a hierarchy.
3. Model-based clustering models data using statistical distributions to find the best fitting model.
It then provides examples of specific clustering algorithms like K-Means, Fuzzy K-Means, Streaming K-Means, Spectral clustering, and Dirichlet clustering.

Clustering

This document discusses unsupervised machine learning classification through clustering. It defines clustering as the process of grouping similar items together, with high intra-cluster similarity and low inter-cluster similarity. The document outlines common clustering algorithms like K-means and hierarchical clustering, and describes how K-means works by assigning points to centroids and iteratively updating centroids. It also discusses applications of clustering in domains like marketing, astronomy, genomics and more.

Semantic net in AI

1) This document discusses semantic networks, which are a knowledge representation technique used in artificial intelligence. Semantic networks represent knowledge through nodes and links, where nodes represent concepts or objects, and links represent relationships between the nodes.
2) As an example, a simple semantic network is presented representing facts about a cat named Jerry - that Jerry is a cat, a mammal, owned by Jay, white in color, and likes cheese.
3) The document outlines different types of semantic networks including definitional, assertional, implicational, and learning networks. It also discusses advantages such as being a natural representation of knowledge, and disadvantages including lack of quantifiers and lack of intelligence.

Dempster shafer theory

The Dempster-Shafer Theory was developed by Arthur Dempster in 1967 and Glenn Shafer in 1976 as an alternative to Bayesian probability. It allows one to combine evidence from different sources and obtain a degree of belief (or probability) for some event. The theory uses belief functions and plausibility functions to represent degrees of belief for various hypotheses given certain evidence. It was developed to describe ignorance and consider all possible outcomes, unlike Bayesian probability which only considers single evidence. An example is given of using the theory to determine the murderer in a room with 4 people where the lights went out.

K MEANS CLUSTERING

This document outlines topics to be covered in a presentation on K-means clustering. It will discuss the introduction of K-means clustering, how the algorithm works, provide an example, and applications. The key aspects are that K-means clustering partitions data into K clusters based on similarity, assigns data points to the closest centroid, and recalculates centroids until clusters are stable. It is commonly used for market segmentation, computer vision, astronomy, and agriculture.

K - Nearest neighbor ( KNN )

Machine learning algorithm ( KNN ) for classification and regression .
Lazy learning , competitive and Instance based learning.

K-means clustering algorithm

K-means clustering is an algorithm that groups data points into k clusters based on their similarity, with each point assigned to the cluster with the nearest mean. It works by randomly selecting k cluster centroids and then iteratively assigning data points to the closest centroid and recalculating the centroids until convergence. K-means clustering is fast, efficient, and commonly used for vector quantization, image segmentation, and discovering customer groups in marketing. Its runtime complexity is O(t*k*n) where t is the number of iterations, k is the number of clusters, and n is the number of data points.

Birch Algorithm With Solved Example

BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data-mining algorithm used to perform hierarchical clustering over, particularly large data sets.

Data warehouse architecture

The document provides information about what a data warehouse is and why it is important. A data warehouse is a relational database designed for querying and analysis that contains historical data from transaction systems and other sources. It allows organizations to access, analyze, and report on integrated information to support business processes and decisions.

Cluster Analysis

Cluster analysis is used to group similar objects together and separate dissimilar objects. It has applications in understanding data patterns and reducing large datasets. The main types are partitional which divides data into non-overlapping subsets, and hierarchical which arranges clusters in a tree structure. Popular clustering algorithms include k-means, hierarchical clustering, and graph-based clustering. K-means partitions data into k clusters by minimizing distances between points and cluster centroids, but requires specifying k and is sensitive to initial centroid positions. Hierarchical clustering creates nested clusters without needing to specify the number of clusters, but has higher computational costs.

3. mining frequent patterns

The document discusses frequent pattern mining and the Apriori algorithm. It introduces frequent patterns as frequently occurring sets of items in transaction data. The Apriori algorithm is described as a seminal method for mining frequent itemsets via multiple passes over the data, generating candidate itemsets and pruning those that are not frequent. Challenges with Apriori include multiple database scans and large number of candidate sets generated.

Dynamic Itemset Counting

Dynamic Itemset Counting (DIC) is an algorithm for efficiently mining frequent itemsets from transactional data that improves upon the Apriori algorithm. DIC allows itemsets to begin being counted as soon as it is suspected they may be frequent, rather than waiting until the end of each pass like Apriori. DIC uses different markings like solid/dashed boxes and circles to track the counting status of itemsets. It can generate frequent itemsets and association rules using conviction in fewer passes over the data compared to Apriori.

Clustering, k-means clustering

The document discusses K-means clustering, an unsupervised machine learning algorithm that partitions observations into k clusters defined by centroids. It compares clustering to classification, noting clustering does not use training data and maps observations into natural groupings. The K-means algorithm is then explained, with the steps of initializing centroids, assigning observations to the closest centroid, revising centroids as cluster means, and repeating until convergence. Applications of clustering in business contexts like banking, retail, and insurance are also briefly mentioned.

K means clustering

K-means clustering is an algorithm that groups data points into k clusters based on their attributes and distances from initial cluster center points. It works by first randomly selecting k data points as initial centroids, then assigning all other points to the closest centroid and recalculating the centroids. This process repeats until the centroids are stable or a maximum number of iterations is reached. K-means clustering is widely used for machine learning applications like image segmentation and speech recognition due to its efficiency, but it is sensitive to initialization and assumes spherical clusters of similar size and density.

Instance Based Learning in Machine Learning

Slides were formed by referring to the text Machine Learning by Tom M Mitchelle (Mc Graw Hill, Indian Edition) and by referring to Video tutorials on NPTEL

Clustering in data Mining (Data Mining)

It is a data mining technique used to place the data elements into their related groups. Clustering is the process of partitioning the data (or objects) into the same class, The data in one class is more similar to each other than to those in other cluster.

3.4 density and grid methods

The document discusses several density-based and grid-based clustering algorithms. DBSCAN is described as a density-based method that forms clusters as maximal sets of density-connected points. OPTICS extends DBSCAN to produce a special ordering of the database with respect to density-based clustering structure. DENCLUE uses density functions to allow mathematically describing arbitrarily shaped clusters. Grid-based methods like STING, WaveCluster, and CLIQUE partition space into a grid structure to perform fast clustering.

3.7 outlier analysis

Outlier analysis identifies outliers, which are data objects that are grossly different from or inconsistent with the remaining set of data. Outliers can be identified using statistical, distance-based, density-based, or deviation-based approaches. Statistical approaches assume an underlying data distribution and identify outliers based on significance probabilities. Distance-based approaches identify outliers as objects with too few neighbors within a given distance. Density-based approaches identify local outliers based on local density comparisons. Deviation-based approaches identify outliers as objects that deviate from the main characteristics of their data group.

Mining Frequent Patterns, Association and Correlations

This document summarizes Chapter 6 of the book "Data Mining: Concepts and Techniques" which discusses frequent pattern mining. It introduces basic concepts like frequent itemsets and association rules. It then describes several scalable algorithms for mining frequent itemsets, including Apriori, FP-Growth, and ECLAT. It also discusses optimizations to Apriori like partitioning the database and techniques to reduce the number of candidates and database scans.

Clock synchronization in distributed system

This document discusses several techniques for clock synchronization in distributed systems:
1. Time stamping events and messages with logical clocks to determine partial ordering without a global clock. Logical clocks assign monotonically increasing sequence numbers.
2. Clock synchronization algorithms like NTP that regularly adjust system clocks across the network to synchronize with a time server. NTP uses averaging to account for network delays.
3. Lamport's logical clocks algorithm that defines "happened before" relations and increments clocks between events to synchronize logical clocks across processes.

Clustering: Large Databases in data mining

The document discusses different approaches for clustering large databases, including divide-and-conquer, incremental, and parallel clustering. It describes three major scalable clustering algorithms: BIRCH, which incrementally clusters incoming records and organizes clusters in a tree structure; CURE, which uses a divide-and-conquer approach to partition data and cluster subsets independently; and DBSCAN, a density-based algorithm that groups together densely populated areas of points.

01 Data Mining: Concepts and Techniques, 2nd ed.

The document provides an overview of data mining concepts and techniques. It introduces data mining, describing it as the process of discovering interesting patterns or knowledge from large amounts of data. It discusses why data mining is necessary due to the explosive growth of data and how it relates to other fields like machine learning, statistics, and database technology. Additionally, it covers different types of data that can be mined, functionalities of data mining like classification and prediction, and classifications of data mining systems.

Types of clustering and different types of clustering algorithms

The document discusses different types of clustering algorithms:
1. Hard clustering assigns each data point to one cluster, while soft clustering allows points to belong to multiple clusters.
2. Hierarchical clustering builds clusters hierarchically in a top-down or bottom-up approach, while flat clustering does not have a hierarchy.
3. Model-based clustering models data using statistical distributions to find the best fitting model.
It then provides examples of specific clustering algorithms like K-Means, Fuzzy K-Means, Streaming K-Means, Spectral clustering, and Dirichlet clustering.

Clustering

This document discusses unsupervised machine learning classification through clustering. It defines clustering as the process of grouping similar items together, with high intra-cluster similarity and low inter-cluster similarity. The document outlines common clustering algorithms like K-means and hierarchical clustering, and describes how K-means works by assigning points to centroids and iteratively updating centroids. It also discusses applications of clustering in domains like marketing, astronomy, genomics and more.

Semantic net in AI

1) This document discusses semantic networks, which are a knowledge representation technique used in artificial intelligence. Semantic networks represent knowledge through nodes and links, where nodes represent concepts or objects, and links represent relationships between the nodes.
2) As an example, a simple semantic network is presented representing facts about a cat named Jerry - that Jerry is a cat, a mammal, owned by Jay, white in color, and likes cheese.
3) The document outlines different types of semantic networks including definitional, assertional, implicational, and learning networks. It also discusses advantages such as being a natural representation of knowledge, and disadvantages including lack of quantifiers and lack of intelligence.

Dempster shafer theory

The Dempster-Shafer Theory was developed by Arthur Dempster in 1967 and Glenn Shafer in 1976 as an alternative to Bayesian probability. It allows one to combine evidence from different sources and obtain a degree of belief (or probability) for some event. The theory uses belief functions and plausibility functions to represent degrees of belief for various hypotheses given certain evidence. It was developed to describe ignorance and consider all possible outcomes, unlike Bayesian probability which only considers single evidence. An example is given of using the theory to determine the murderer in a room with 4 people where the lights went out.

K MEANS CLUSTERING

This document outlines topics to be covered in a presentation on K-means clustering. It will discuss the introduction of K-means clustering, how the algorithm works, provide an example, and applications. The key aspects are that K-means clustering partitions data into K clusters based on similarity, assigns data points to the closest centroid, and recalculates centroids until clusters are stable. It is commonly used for market segmentation, computer vision, astronomy, and agriculture.

K - Nearest neighbor ( KNN )

Machine learning algorithm ( KNN ) for classification and regression .
Lazy learning , competitive and Instance based learning.

K-means clustering algorithm

K-means clustering is an algorithm that groups data points into k clusters based on their similarity, with each point assigned to the cluster with the nearest mean. It works by randomly selecting k cluster centroids and then iteratively assigning data points to the closest centroid and recalculating the centroids until convergence. K-means clustering is fast, efficient, and commonly used for vector quantization, image segmentation, and discovering customer groups in marketing. Its runtime complexity is O(t*k*n) where t is the number of iterations, k is the number of clusters, and n is the number of data points.

Birch Algorithm With Solved Example

BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data-mining algorithm used to perform hierarchical clustering over, particularly large data sets.

3. mining frequent patterns

3. mining frequent patterns

Dynamic Itemset Counting

Dynamic Itemset Counting

Clustering, k-means clustering

Clustering, k-means clustering

K means clustering

K means clustering

Instance Based Learning in Machine Learning

Instance Based Learning in Machine Learning

Clustering in data Mining (Data Mining)

Clustering in data Mining (Data Mining)

3.4 density and grid methods

3.4 density and grid methods

3.7 outlier analysis

3.7 outlier analysis

Mining Frequent Patterns, Association and Correlations

Mining Frequent Patterns, Association and Correlations

Clock synchronization in distributed system

Clock synchronization in distributed system

Clustering: Large Databases in data mining

Clustering: Large Databases in data mining

01 Data Mining: Concepts and Techniques, 2nd ed.

01 Data Mining: Concepts and Techniques, 2nd ed.

Types of clustering and different types of clustering algorithms

Types of clustering and different types of clustering algorithms

Clustering

Clustering

Semantic net in AI

Semantic net in AI

Dempster shafer theory

Dempster shafer theory

K MEANS CLUSTERING

K MEANS CLUSTERING

K - Nearest neighbor ( KNN )

K - Nearest neighbor ( KNN )

K-means clustering algorithm

K-means clustering algorithm

Birch Algorithm With Solved Example

Birch Algorithm With Solved Example

Data warehouse architecture

The document provides information about what a data warehouse is and why it is important. A data warehouse is a relational database designed for querying and analysis that contains historical data from transaction systems and other sources. It allows organizations to access, analyze, and report on integrated information to support business processes and decisions.

Cluster Analysis

Cluster analysis is used to group similar objects together and separate dissimilar objects. It has applications in understanding data patterns and reducing large datasets. The main types are partitional which divides data into non-overlapping subsets, and hierarchical which arranges clusters in a tree structure. Popular clustering algorithms include k-means, hierarchical clustering, and graph-based clustering. K-means partitions data into k clusters by minimizing distances between points and cluster centroids, but requires specifying k and is sensitive to initial centroid positions. Hierarchical clustering creates nested clusters without needing to specify the number of clusters, but has higher computational costs.

Cluster analysis

The document provides an overview of topics to be covered in a data analysis course, including cluster analysis and decision trees. The course will cover descriptive statistics, probability distributions, correlation, regression, hypothesis testing, clustering methods like k-means, and decision tree techniques like CHAID. Clustering involves grouping similar objects together to identify homogeneous clusters that are heterogeneous from each other. Applications of clustering include market segmentation, credit risk analysis, and operations. The document gives an example of clustering students based on their exam scores.

Clique

CLIQUE is a grid-based clustering algorithm that identifies dense units in subspaces of high-dimensional data to provide efficient clustering. It works by first partitioning each attribute dimension into equal intervals and then the data space into rectangular grid cells. It finds dense units in subspaces like planes and intersections them to identify dense units in higher dimensions. These dense units are grouped into clusters. CLIQUE scales linearly with size of data and number of dimensions and automatically identifies relevant subspaces for clustering. However, the clustering accuracy may be reduced for simplicity.

Difference between molap, rolap and holap in ssas

MOLAP, ROLAP, and HOLAP are different storage modes in SQL Server Analysis Services (SSAS). MOLAP stores aggregated data and source data in a multidimensional structure for fast queries. ROLAP stores aggregations in indexed views in the relational database, while HOLAP combines MOLAP and ROLAP by storing aggregations in a multidimensional structure but not source data. Queries against aggregated data are faster with MOLAP and HOLAP, while ROLAP is slower but uses less storage.

Database aggregation using metadata

This document describes a simulator for database aggregation using metadata. The simulator sits between an end-user application and a database management system (DBMS) to intercept SQL queries and transform them to take advantage of available aggregates using metadata describing the data warehouse schema. The simulator provides performance gains by optimizing queries to use appropriate aggregate tables. It was found to improve performance over previous aggregate navigators by making fewer calls to system tables through the use of metadata mappings. Experimental results showed the simulator solved queries faster than alternative approaches by transforming queries to leverage aggregate tables.

Data preprocessing

Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/

Density Based Clustering

This document summarizes the DBSCAN clustering algorithm. DBSCAN finds clusters based on density, requiring only two parameters: Eps, which defines the neighborhood distance, and MinPts, the minimum number of points required to form a cluster. It can discover clusters of arbitrary shape. The algorithm works by expanding clusters from core points, which have at least MinPts points within their Eps-neighborhood. Points that are not part of any cluster are classified as noise. Applications include spatial data analysis, image segmentation, and automatic border detection in medical images.

Application of data mining

Shivani Soni presented on data mining. Data mining involves using computational methods to discover patterns in large datasets, combining techniques from machine learning, statistics, artificial intelligence, and database systems. It is used to extract useful information from data and transform it into an understandable structure. Data mining has various applications, including in sales/marketing, banking/finance, healthcare/insurance, transportation, medicine, education, manufacturing, and research analysis. It enables businesses to understand customer purchasing patterns and maximize profits. Examples of its use include fraud detection, credit risk analysis, stock trading, customer loyalty analysis, distribution scheduling, claims analysis, risk profiling, detecting medical therapy patterns, education decision making, and aiding manufacturing process design and research.

Apriori Algorithm

The document discusses the Apriori algorithm, which is used for mining frequent itemsets from transactional databases. It begins with an overview and definition of the Apriori algorithm and its key concepts like frequent itemsets, the Apriori property, and join operations. It then outlines the steps of the Apriori algorithm, provides an example using a market basket database, and includes pseudocode. The document also discusses limitations of the algorithm and methods to improve its efficiency, as well as advantages and disadvantages.

Cluster analysis

Cluster analysis is a technique used to group objects based on characteristics they possess. It involves measuring the distance or similarity between objects and grouping those that are most similar together. There are two main types: hierarchical cluster analysis, which groups objects sequentially into clusters; and nonhierarchical cluster analysis, which directly assigns objects to pre-specified clusters. The choice of method depends on factors like sample size and research objectives.

OLAP

OLAP provides multidimensional analysis of large datasets to help solve business problems. It uses a multidimensional data model to allow for drilling down and across different dimensions like students, exams, departments, and colleges. OLAP tools are classified as MOLAP, ROLAP, or HOLAP based on how they store and access multidimensional data. MOLAP uses a multidimensional database for fast performance while ROLAP accesses relational databases through metadata. HOLAP provides some analysis directly on relational data or through intermediate MOLAP storage. Web-enabled OLAP allows interactive querying over the internet.

Data Mining: Association Rules Basics

Association rule mining finds frequent patterns and correlations among items in transaction databases. It involves two main steps:
1) Frequent itemset generation: Finds itemsets that occur together in a minimum number of transactions (above a support threshold). This is done efficiently using the Apriori algorithm.
2) Rule generation: Generates rules from frequent itemsets where the confidence (fraction of transactions with left hand side that also contain right hand side) is above a minimum threshold. Rules are a partitioning of an itemset into left and right sides.

Data warehouse architecture

Data warehouse architecture

Cluster Analysis

Cluster Analysis

Cluster analysis

Cluster analysis

Clique

Clique

Difference between molap, rolap and holap in ssas

Difference between molap, rolap and holap in ssas

Database aggregation using metadata

Database aggregation using metadata

Data preprocessing

Data preprocessing

Density Based Clustering

Density Based Clustering

Application of data mining

Application of data mining

Apriori Algorithm

Apriori Algorithm

Cluster analysis

Cluster analysis

OLAP

OLAP

Data Mining: Association Rules Basics

Data Mining: Association Rules Basics

Cluster analysis

This is Cluster Analysis Slide which help you to understand detailed note about cluster analysis and their Categories.

An Efficient Clustering Method for Aggregation on Data Fragments

Clustering is an important step in the process of data analysis with applications to numerous fields. Clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a quality cluster. Existing clustering aggregation algorithms are applied directly to large number of data points. The algorithms are inefficient if the number of data points is large. This project defines an efficient approach for clustering aggregation based on data fragments. In fragment-based approach, a data fragment is any subset of the data. To increase the efficiency of the proposed approach, the clustering aggregation can be performed directly on data fragments under comparison measure and normalized mutual information measures for clustering aggregation, enhanced clustering aggregation algorithms are described. To show the minimal computational complexity. (Agglomerative, Furthest, and Local Search); nevertheless, which increases the accuracy.

Extended pso algorithm for improvement problems k means clustering algorithm

The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.

Extended pso algorithm for improvement problems k means clustering algorithm

The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.

Rohit 10103543

The document discusses clustering and its applications in contour detection. It notes that while clustering is widely used to organize unlabeled data and remove noise, there are still challenges. Specifically, selecting an appropriate data set, determining the number of clusters, and validating results can be ambiguous. Clustering algorithms are also sensitive to these parameters and the data set properties. Contour extraction methods also lack efficiency and universality. Improved clustering techniques are needed that can be more effectively applied to contour detection problems across different data sets.

Data clustering using kernel based

In recent machine learning community, there is a trend of constructing a linear logarithm version of
nonlinear version through the ‘kernel method’ for example kernel principal component analysis, kernel
fisher discriminant analysis, support Vector Machines (SVMs), and the current kernel clustering
algorithms. Typically, in unsupervised methods of clustering algorithms utilizing kernel method, a
nonlinear mapping is operated initially in order to map the data into a much higher space feature, and then
clustering is executed. A hitch of these kernel clustering algorithms is that the clustering prototype resides
in increased features specs of dimensions and therefore lack intuitive and clear descriptions without
utilizing added approximation of projection from the specs to the data as executed in the literature
presented. This paper aims to utilize the ‘kernel method’, a novel clustering algorithm, founded on the
conventional fuzzy clustering algorithm (FCM) is anticipated and known as kernel fuzzy c-means algorithm
(KFCM). This method embraces a novel kernel-induced metric in the space of data in order to interchange
the novel Euclidean matric norm in cluster prototype and fuzzy clustering algorithm still reside in the space
of data so that the results of clustering could be interpreted and reformulated in the spaces which are
original. This property is used for clustering incomplete data. Execution on supposed data illustrate that
KFCM has improved performance of clustering and stout as compare to other transformations of FCM for
clustering incomplete data.

Experimental study of Data clustering using k- Means and modified algorithms

The k- Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease
and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in
exploratory data analysis. This paper presents results of the experimental study of different approaches to
k- Means clustering, thereby comparing results on different datasets using Original k-Means and other
modified algorithms implemented using MATLAB R2009b. The results are calculated on some performance
measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity index and
execution time

50120140505013

This document describes a new distance-based clustering algorithm (DBCA) that aims to improve upon K-means clustering. DBCA selects initial cluster centroids based on the total distance of each data point to all other points, rather than random selection. It calculates distances between all points, identifies points with maximum total distances, and sets initial centroids as the averages of groups of these maximally distant points. The algorithm is compared to K-means, hierarchical clustering, and hierarchical partitioning clustering on synthetic and real data. Experimental results show DBCA produces better quality clusters than these other algorithms.

A PSO-Based Subtractive Data Clustering Algorithm

There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studies have shown that partitional clustering algorithms such as the k-means algorithm are the most popular algorithms for clustering large datasets. The major problem with partitional clustering algorithms is that they are sensitive to the selection of the initial partitions and are prone to premature converge to local optima. Subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and cluster centers for any given set of data. The cluster estimates can be used to initialize iterative optimization-based clustering methods and model identification methods. In this paper, we present a hybrid Particle Swarm Optimization, Subtractive + (PSO) clustering algorithm that performs fast clustering. For comparison purpose, we applied the Subtractive + (PSO) clustering algorithm, PSO, and the Subtractive clustering algorithms on three different datasets. The results illustrate that the Subtractive + (PSO) clustering algorithm can generate the most compact clustering results as compared to other algorithms.

Automated Clustering Project - 12th CONTECSI 34th WCARS

This document describes an automated clustering and outlier detection program. The program normalizes data, performs principal component analysis to select important components, compares clustering algorithms, selects the best model using silhouette values, and produces outputs labeling clusters and outliers. It is demonstrated on a sample of 5,000 credit card customer records, identifying a small cluster of 3 accounts as outliers based on features like new status and high late payments.

Enhanced Clustering Algorithm for Processing Online Data

This document proposes an enhanced incremental clustering algorithm for processing online data. It discusses existing clustering algorithms like leader clustering and hierarchical clustering, which have limitations in handling dynamic data. The proposed algorithm aims to dynamically create initial clusters and rearrange clusters based on data characteristics, allowing for more accurate clustering of online data over time. It also introduces a new frequency-based method for searching and retrieving specific data from fixed clusters.

Big data Clustering Algorithms And Strategies

The document discusses various algorithms for big data clustering. It begins by covering preprocessing techniques such as data reduction. It then covers hierarchical, prototype-based, density-based, grid-based, and scalability clustering algorithms. Specific algorithms discussed include K-means, K-medoids, PAM, CLARA/CLARANS, DBSCAN, OPTICS, MR-DBSCAN, DBCURE, and hierarchical algorithms like PINK and l-SL. The document emphasizes techniques for scaling these algorithms to large datasets, including partitioning, sampling, approximation strategies, and MapReduce implementations.

CLUSTERING IN DATA MINING.pdf

Clustering is an unsupervised machine learning technique used to group unlabeled data points. There are two main approaches: hierarchical clustering and partitioning clustering. Partitioning clustering algorithms like k-means and k-medoids attempt to partition data into k clusters by optimizing a criterion function. Hierarchical clustering creates nested clusters by merging or splitting clusters. Examples of hierarchical algorithms include agglomerative clustering, which builds clusters from bottom-up, and divisive clustering, which separates clusters from top-down. Clustering can group both numerical and categorical data.

A0310112

The document provides a literature review of different clustering techniques. It begins by defining clustering and its applications. It then categorizes and describes several clustering methods including hierarchical (BIRCH, CURE, CHAMELEON), partitioning (k-means, k-medoids), density-based (DBSCAN, OPTICS, DENCLUE), grid-based (CLIQUE, STING, MAFIA), and model-based (RBMN, SOM) methods. For each method, it discusses the algorithm, advantages, disadvantages and time complexity. The document aims to provide an overview of various clustering techniques for classification and comparison.

Comparison Between Clustering Algorithms for Microarray Data Analysis

Currently, there are two techniques used for large-scale gene-expression profiling; microarray and
RNA-Sequence (RNA-Seq).This paper is intended to study and compare different clustering algorithms that used
in microarray data analysis. Microarray is a DNA molecules array which allows multiple hybridization
experiments to be carried out simultaneously and trace expression levels of thousands of genes. It is a highthroughput
technology for gene expression analysis and becomes an effective tool for biomedical research.
Microarray analysis aims to interpret the data produced from experiments on DNA, RNA, and protein
microarrays, which enable researchers to investigate the expression state of a large number of genes. Data
clustering represents the first and main process in microarray data analysis. The k-means, fuzzy c-mean, selforganizing
map, and hierarchical clustering algorithms are under investigation in this paper. These algorithms
are compared based on their clustering model.

Unsupervised Learning.pptx

Model Selection and Evaluation
Dimensionality Reduction
Artificial intelligence
Machine Learning
Supervised Learning
Unsupervised Learning

Clustering and Classification Algorithms Ankita Dubey

Clustering is a process of partitioning a set of data (or objects) into a set of meaningful sub-classes, called clusters. Help users understand the natural grouping or structure in a data set. Used either as a stand-alone tool to get insight into data distribution or as a preprocessing step for other algorithms.

D0931621

1. The document presents a hybrid algorithm that combines Kernelized Fuzzy C-Means (KFCM), Hybrid Ant Colony Optimization (HACO), and Fuzzy Adaptive Particle Swarm Optimization (FAPSO) to improve clustering of electrocardiogram (ECG) beat data.
2. The algorithm maps data into a higher dimensional space using kernel functions to make clusters more linearly separable, addresses issues with KFCM being sensitive to initialization and prone to local minima.
3. It uses HACO to optimize cluster centers and membership degrees, and FAPSO to evaluate fitness values and optimize weight vectors, forming usable clusters for applications like ECG classification.

84cc04ff77007e457df6aa2b814d2346bf1b

This document compares hierarchical and non-hierarchical clustering algorithms. It summarizes four clustering algorithms: K-Means, K-Medoids, Farthest First Clustering (hierarchical algorithms), and DBSCAN (non-hierarchical algorithm). It describes the methodology of each algorithm and provides pseudocode. It also describes the datasets used to evaluate the performance of the algorithms and the evaluation metrics. The goal is to compare the performance of the clustering methods on different datasets.

Ijetr021251

Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org

Cluster analysis

Cluster analysis

An Efficient Clustering Method for Aggregation on Data Fragments

An Efficient Clustering Method for Aggregation on Data Fragments

Extended pso algorithm for improvement problems k means clustering algorithm

Extended pso algorithm for improvement problems k means clustering algorithm

Extended pso algorithm for improvement problems k means clustering algorithm

Extended pso algorithm for improvement problems k means clustering algorithm

Rohit 10103543

Rohit 10103543

Data clustering using kernel based

Data clustering using kernel based

Experimental study of Data clustering using k- Means and modified algorithms

Experimental study of Data clustering using k- Means and modified algorithms

50120140505013

50120140505013

A PSO-Based Subtractive Data Clustering Algorithm

A PSO-Based Subtractive Data Clustering Algorithm

Automated Clustering Project - 12th CONTECSI 34th WCARS

Automated Clustering Project - 12th CONTECSI 34th WCARS

Enhanced Clustering Algorithm for Processing Online Data

Enhanced Clustering Algorithm for Processing Online Data

Big data Clustering Algorithms And Strategies

Big data Clustering Algorithms And Strategies

CLUSTERING IN DATA MINING.pdf

CLUSTERING IN DATA MINING.pdf

A0310112

A0310112

Comparison Between Clustering Algorithms for Microarray Data Analysis

Comparison Between Clustering Algorithms for Microarray Data Analysis

Unsupervised Learning.pptx

Unsupervised Learning.pptx

Clustering and Classification Algorithms Ankita Dubey

Clustering and Classification Algorithms Ankita Dubey

D0931621

D0931621

84cc04ff77007e457df6aa2b814d2346bf1b

84cc04ff77007e457df6aa2b814d2346bf1b

Ijetr021251

Ijetr021251

Music Motive @ H-ack

Progetto Vincitore del primo H-ack in H-farm
by Chiara Olivieri, Giovanni Trento, Andrea Bazerla, Lino Possamai, Walter Barbagallo, Nicola Ghirardi, Enrico Battistelli, Giovanna Nardini, Alessandro Paolini
http://ghirardinicola.blogspot.it/2013/10/and-winner-is-team-fungo-hackindustry.html

Metodi matematici per l’analisi di sistemi complessi

Introduzione alle strutture dati, ai metodi matematici, algoritmi e dati empirici su alcuni sistemi complessi presenti in natura.

Multidimensional Analysis of Complex Networks

A new study of how complex networks evolve along the two most important informative axes, space and time.

Slashdot.Org

Lecture about slashdot business model

Optimization of Collective Communication in MPICH

This is a lecture about the paper: "Optimization of Collective Communication in MPICH". Department of Computer Science, University Ca' Foscari of Venice, Italy

A static Analyzer for Finding Dynamic Programming Errors

A static Analyzer for Finding Dynamic Programming Errors

On Applying Or-Parallelism and Tabling to Logic Programs

The document discusses applying or-parallelism and tabling techniques to logic programs to improve performance. Or-parallelism allows concurrent execution of alternatives by distributing subgoals across multiple engines. Tabling remembers prior computations to avoid redundant evaluations and ensures termination for some non-terminating programs. The authors propose a model that combines or-parallelism within tabling to leverage both techniques for efficient parallel execution.

Music Motive @ H-ack

Music Motive @ H-ack

Metodi matematici per l’analisi di sistemi complessi

Metodi matematici per l’analisi di sistemi complessi

Multidimensional Analysis of Complex Networks

Multidimensional Analysis of Complex Networks

Slashdot.Org

Slashdot.Org

Optimization of Collective Communication in MPICH

Optimization of Collective Communication in MPICH

A static Analyzer for Finding Dynamic Programming Errors

A static Analyzer for Finding Dynamic Programming Errors

On Applying Or-Parallelism and Tabling to Logic Programs

On Applying Or-Parallelism and Tabling to Logic Programs

Astute Business Solutions | Oracle Cloud Partner |

Your goto partner for Oracle Cloud, PeopleSoft, E-Business Suite, and Ellucian Banner. We are a firm specialized in managed services and consulting.

Dandelion Hashtable: beyond billion requests per second on a commodity server

This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).

How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf

A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.

Choosing The Best AWS Service For Your Website + API.pptx

Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!

Y-Combinator seed pitch deck template PP

Pitch Deck Template

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...

Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257

High performance Serverless Java on AWS- GoTo Amsterdam 2024

Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.

Essentials of Automations: Exploring Attributes & Automation Parameters

Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.

Nordic Marketo Engage User Group_June 13_ 2024.pptx

Slides from event

Leveraging the Graph for Clinical Trials and Standards

Katja Glaß
OpenStudyBuilder Community Manager - Katja Glaß Consulting
Marius Conjeaud
Principal Consultant - Neo4j

"Choosing proper type of scaling", Olena Syrota

Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.

A Deep Dive into ScyllaDB's Architecture

This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.

inQuba Webinar Mastering Customer Journey Management with Dr Graham Hill

HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable.
In this webinar, we explored essential aspects of Customer Journey Management and personalization. Here’s a summary of the key insights and topics discussed:
Key Takeaways:
Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement.
Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers.
Technology Integration: Insights were shared on how inQuba’s advanced technology can streamline customer interactions and drive operational efficiency.

Apps Break Data

How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?

"Scaling RAG Applications to serve millions of users", Kevin Goedecke

How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.

Fueling AI with Great Data with Airbyte Webinar

This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.

GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph

Tomaz Bratanic
Graph ML and GenAI Expert - Neo4j

[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...

The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.

GNSS spoofing via SDR (Criptored Talks 2024)

In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.

Astute Business Solutions | Oracle Cloud Partner |

Astute Business Solutions | Oracle Cloud Partner |

Dandelion Hashtable: beyond billion requests per second on a commodity server

Dandelion Hashtable: beyond billion requests per second on a commodity server

How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf

How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf

Choosing The Best AWS Service For Your Website + API.pptx

Choosing The Best AWS Service For Your Website + API.pptx

Y-Combinator seed pitch deck template PP

Y-Combinator seed pitch deck template PP

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...

High performance Serverless Java on AWS- GoTo Amsterdam 2024

High performance Serverless Java on AWS- GoTo Amsterdam 2024

9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...

9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...

Essentials of Automations: Exploring Attributes & Automation Parameters

Essentials of Automations: Exploring Attributes & Automation Parameters

Nordic Marketo Engage User Group_June 13_ 2024.pptx

Nordic Marketo Engage User Group_June 13_ 2024.pptx

Leveraging the Graph for Clinical Trials and Standards

Leveraging the Graph for Clinical Trials and Standards

"Choosing proper type of scaling", Olena Syrota

"Choosing proper type of scaling", Olena Syrota

A Deep Dive into ScyllaDB's Architecture

A Deep Dive into ScyllaDB's Architecture

inQuba Webinar Mastering Customer Journey Management with Dr Graham Hill

inQuba Webinar Mastering Customer Journey Management with Dr Graham Hill

Apps Break Data

Apps Break Data

"Scaling RAG Applications to serve millions of users", Kevin Goedecke

"Scaling RAG Applications to serve millions of users", Kevin Goedecke

Fueling AI with Great Data with Airbyte Webinar

Fueling AI with Great Data with Airbyte Webinar

GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph

GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph

[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...

[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...

GNSS spoofing via SDR (Criptored Talks 2024)

GNSS spoofing via SDR (Criptored Talks 2024)

- 1. Cure: An Efficient Clustering Algorithm for Large Databases Possamai Lino, 800509 Department of Computer Science University of Venice www.possamai.it/lino Data Mining Lecture - September 13th, 2006
- 10. Example