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.
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
DATA
Data is any raw material or unorganized information.
CLUSTER
Cluster is group of objects that belongs to a same class.
Cluster is a set of tables physically stored together as one table that shares common columns.
http://phpexecutor.com
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/
UNIT - 4: Data Warehousing and Data MiningNandakumar P
UNIT-IV
Cluster Analysis: Types of Data in Cluster Analysis – A Categorization of Major Clustering Methods – Partitioning Methods – Hierarchical methods – Density, Based Methods – Grid, Based Methods – Model, Based Clustering Methods – Clustering High, Dimensional Data – Constraint, Based Cluster Analysis – Outlier Analysis.
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
DATA
Data is any raw material or unorganized information.
CLUSTER
Cluster is group of objects that belongs to a same class.
Cluster is a set of tables physically stored together as one table that shares common columns.
http://phpexecutor.com
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/
UNIT - 4: Data Warehousing and Data MiningNandakumar P
UNIT-IV
Cluster Analysis: Types of Data in Cluster Analysis – A Categorization of Major Clustering Methods – Partitioning Methods – Hierarchical methods – Density, Based Methods – Grid, Based Methods – Model, Based Clustering Methods – Clustering High, Dimensional Data – Constraint, Based Cluster Analysis – Outlier Analysis.
Mastering Hierarchical Clustering: A Comprehensive Guidecyberprosocial
In the world of data analysis and machine learning, hierarchical clustering is a really important technique that helps us understand how different pieces of data are related to each other. This article is here to explain hierarchical clustering in a way that’s easy to understand, breaking down its main ideas, how it’s used, and the benefits it brings.
Clustering: Introduction, Types of clustering;
Partition-based clustering: K-Means, K-Medoids;
Density based clustering: DBSCAN, Clustering evaluation.
Mining Data Stream, Mining Time-Series Data, Mining Sequence Patterns in Transactional Database,
Social Network analysis and Multirelational Data Mining.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
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Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
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From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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Business update Q1 2024 Lar España Real Estate SOCIMI
Clustering in data Mining (Data Mining)
1.
2. Atta Ul Mustafa
Armgan Ali
Ali raza
Atif Ali
Abdul Rehman
(4425)
4424)
(4427)
(4407)
(4403)
3. Introduction
Clustering
Why Clustering?
Types of clustering
Methods of clustering
Applications of clustering
4. Clustering is the process of making a group of
abstract objects into classes of similar objects.
Points to Remember
A cluster of data objects can be treated as one
group.
While doing cluster analysis, we first partition the
set of data into groups based on data similarity
and then assign the labels to the groups.
The main advantage of clustering over
classification is that, it is adaptable to changes
and helps single out useful features that
distinguish different groups.
5.
6. High dimensionality - The clustering
algorithm should not only be able to handle
low- dimensional data but also the high
dimensional space.
Ability to deal with noisy data - Databases
contain noisy, missing or erroneous data.
Some algorithms are sensitive to such data
and may lead to poor quality clusters.
Interpretability - The clustering results
should be interpretable, comprehensible and
usable.
7. Scalability - We need highly scalable
clustering algorithms to deal with large
databases.
Ability to deal with different kind of
attributes Algorithms should be capable to be
applied on any kind of data such as interval
based (numerical) data, categorical, binary
data.
Discovery of clusters with attribute shape -
The clustering algorithm should be capable of
detect cluster of arbitrary shape. It should not
be bounded to only distance measures that
tend to find spherical cluster of small size.
8. Clustering can be divided into different categories
based on different criteria
1.Hard clustering: A given data point in n-
dimensional space only belongs to one cluster. This
is also known as exclusive clustering. The K-Means
clustering mechanism is an example of hard
clustering.
2.Soft clustering: A given data point can belong to
more than one cluster in soft clustering. This is also
known as overlapping clustering. The Fuzzy K-Means
algorithm is a good example of soft clustering.
3.Hierarchial clustering: In hierarchical clustering, a
hierarchy of clusters is built using the top-down
(divisive) or bottom-up (agglomerative) approach.
9. 4. Flat clustering: Is a simple technique
where no hierarchy is present.
5.Model-based clustering: In model-based
clustering, data is modeled using a standard
statistical model to work with different
distributions. The idea is to find a model that
best fits the data.
10. Clustering analysis is broadly used in many applications
such as market research, pattern recognition, data
analysis, and image processing.
Clustering can also help marketers discover distinct
groups in their customer base. And they can characterize
their customer groups based on the purchasing patterns.
In the field of biology, it can be used to derive plant and
animal taxonomies, categorize genes with similar
functionalities and gain insight into structures inherent to
populations.
Clustering also helps in identification of areas of similar
land use in an earth observation database. It also helps in
the identification of groups of houses in a city according
to house type, value, and geographic location.
11. Clustering also helps in classifying
documents on the web for information
discovery.
Clustering is also used in outlier detection
applications such as detection of credit card
fraud.
As a data mining function, cluster analysis
serves as a tool to gain insight into the
distribution of data to observe characteristics
of each cluster.
12. The following points throw light on why
clustering is required in data mining −
Scalability − We need highly scalable clustering
algorithms to deal with large databases.
Ability to deal with different kinds of attributes −
Algorithms should be capable to be applied on
any kind of data such as interval-based
(numerical) data, categorical, and binary data.
Discovery of clusters with attribute shape − The
clustering algorithm should be capable of
detecting clusters of arbitrary shape. They should
not be bounded to only distance measures that
tend to find spherical cluster of small sizes.
13. High dimensionality − The clustering
algorithm should not only be able to handle
low-dimensional data but also the high
dimensional space.
Ability to deal with noisy data − Databases
contain noisy, missing or erroneous data.
Some algorithms are sensitive to such data
and may lead to poor quality clusters.
Interpretability − The clustering results
should be interpretable, comprehensible, and
usable.
14. Clustering methods can be classified into the
following categories −
Partitioning Method
Hierarchical Method
Density-based Method
Grid-Based Method
Model-Based Method
Constraint-based Method
15. Suppose we are given a database of ‘n’ objects and
the partitioning method constructs ‘k’ partition of
data. Each partition will represent a cluster and k ≤ n.
It means that it will classify the data into k groups,
which satisfy the following requirements −
Each group contains at least one object.
Each object must belong to exactly one group.
Points to remember −
For a given number of partitions (say k), the
partitioning method will create an initial partitioning.
Then it uses the iterative relocation technique to
improve the partitioning by moving objects from one
group to other.
16. This method creates a hierarchical
decomposition of the given set of data
objects. We can classify hierarchical methods
on the basis of how the hierarchical
decomposition is formed. There are two
approaches here −
Agglomerative Approach
Divisive Approach
17. Agglomerative Approach
This approach is also known as the bottom-up
approach. In this, we start with each object forming a
separate group. It keeps on merging the objects or
groups that are close to one another. It keep on
doing so until all of the groups are merged into one
or until the termination condition holds.
Divisive Approach
This approach is also known as the top-down
approach. In this, we start with all of the objects in
the same cluster. In the continuous iteration, a
cluster is split up into smaller clusters. It is down
until each object in one cluster or the termination
condition holds. This method is rigid, i.e., once a
merging or splitting is done, it can never be undone.
18. This method is based on the notion of
density. The basic idea is to continue growing
the given cluster as long as the density in the
neighborhood exceeds some threshold, i.e.,
for each data point within a given cluster, the
radius of a given cluster has to contain at
least a minimum number of points.
19. In this, the objects together form a grid. The
object space is quantized into finite number
of cells that form a grid structure.
Advantages
The major advantage of this method is fast
processing time.
It is dependent only on the number of cells in
each dimension in the quantized space.
20. In this method, a model is hypothesized for
each cluster to find the best fit of data for a
given model. This method locates the clusters
by clustering the density function. It reflects
spatial distribution of the data points.
This method also provides a way to
automatically determine the number of
clusters based on standard statistics, taking
outlier or noise into account. It therefore
yields robust clustering methods.
21. In this method, the clustering is performed by
the incorporation of user or application-
oriented constraints. A constraint refers to
the user expectation or the properties of
desired clustering results. Constraints
provide us with an interactive way of
communication with the clustering process.
Constraints can be specified by the user or
the application requirement.