## Cure Algorithm Ques10

Contents

- 1 What is the cure algorithm?
- 2 What is the implementation of cure algorithm?
- 3 What are the 4 main algorithms?
- 4 What are the advantages of cure algorithm?

## What is the cure algorithm?

What is CURE CURE represents Clustering Using Representative. It is a clustering algorithm that uses a multiple techniques to make an approach that can manage high data sets, outliers, and clusters with non-spherical architecture and non-uniform sizes.

CURE defines a cluster by using several representative points from the cluster. These points will taking the geometry and architecture of the cluster. The first representative point is selected to be the point farthest from the middle of the cluster, while the remaining points are selected so that they are farthest from all the earlier selected points.

In this method, the representative points are associatively well distributed. The multiple points chosen is a parameter, but it was discovered that a value of 10 or more operated well. Because the representative points are selected, they are diminished toward the center by a factor,𝛼.

This support moderate the effect of outliers, which are generally further away from the center and therefore, are shrunk more. For instance, a representative point that was a distance of 10 units from the center can change by 3 units (for 𝛼 = 0.7), while a representative point at a distance of 1 unit can change 0.3 units.

CURE takes benefit of specific characteristics of the hierarchical clustering process to remove outliers at two multiple points in the clustering phase. First, if a cluster is increasing slowly, then this can mean that it includes mostly of outliers, because by definition, outliers are far from others and will not be combined with different points very often.

In CURE, this first procedure of outlier elimination generally appears when the number of clusters is 1/3 the initial number of points. The second procedure of outlier elimination appears when the multiple clusters is on the order of K, the multiple desired clusters. At this point, small clusters are removed.

Because the worst-case complexity of CURE is $\mathrm $, it cannot be used precisely to high data sets. CURE uses two methods to speed up the clustering procedure. The first method takes a random sample and implements hierarchical clustering on the sampled data points.

This is followed by a last pass that creates each remaining point in the data set to one of the clusters by selecting the cluster with the nearest representative point. In some cases, the sample needed for clustering is high and a second more technique is needed. In this situation, CURE partitions the sample data and clusters the points in every partition.

This pre-clustering procedure is followed by a clustering of the intermediate clusters and a last pass that creates each point in the data set to one of the clusters. : What is CURE

## What is the implementation of cure algorithm?

CURE algorithm is implemented by the combination of data collection and the data reduction by use of random sampling method and partitioning method.1. Join table A and table B on equivalence of column A and column B 2. Calculate count from result set and store it in a variable T= total number of rows.

#### What is the cure algorithm for clustering large data sets?

To handle large databases, CURE employs a combination of random sampling and par- titioning. A random sample drawn from the data set is first partitioned and each partition is partially clustered. The partial clusters are then clustered in a second pass to yield the desired clusters.

#### What is the complexity of cure algorithm?

The time complexity of CURE is O(n2 log n) in general, and in the case when the data points lie in a two-dimensional space, the time complexity can be shown to reduce to O(n2).

## What are the 4 main algorithms?

A guide to machine learning algorithms and their applications – The term ‘machine learning’ is often, incorrectly, interchanged with Artificial Intelligence, but machine learning is actually a sub field/type of AI. Machine learning is also often referred to as predictive analytics, or predictive modelling.

- Coined by American computer scientist Arthur Samuel in 1959, the term ‘machine learning’ is defined as a “computer’s ability to learn without being explicitly programmed”.
- At its most basic, machine learning uses programmed algorithms that receive and analyse input data to predict output values within an acceptable range.

As new data is fed to these algorithms, they learn and optimise their operations to improve performance, developing ‘intelligence’ over time. There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement.

### What are the 3 rules of algorithm?

Properties of Algorithm: It should terminate after a finite time. It should produce at least one output. It should take zero or more input.

#### What is the difference between Kmeans and cure?

From Wikipedia, the free encyclopedia CURE (Clustering Using REpresentatives) is an efficient data clustering algorithm for large databases, Compared with K-means clustering it is more robust to outliers and able to identify clusters having non-spherical shapes and size variances.

#### What are the advantages of cure algorithm?

CURE can handle large databases efficiently. CURE can effectively detect proper shape of the cluster with the help of scattered representative point and centroid shrinking. CURE can effectively remove outlier.

### Which clustering algorithm is best?

7. Ordering Points To Identify the Structure of Clustering – OPTICS or Ordering Points To Identify the Structure of the Clustering Algorithm has the potential of improving database cataloging. You may ponder what actually is database cataloging !! So, database cataloging is a way of sequentially arranging the list of databases comprising of datasets residing within the clusters.

- These clusters are of variable densities and shapes and hence, their structure varies.
- Furthermore, the basic approach of OPTICS is similar to that of the Density-based Spatial Clustering Algorithm (already discussed in point number 5) but at the same time, many of the DBSCAN’s weaknesses are addressed are meaningfully resolved.

The prime reason for detecting and resolving the DBSCAN’s weaknesses is that now, you need not worry about the identification of more densely populated clusters which wasn’t done by DBSCAN. Wanna see how this algorithm works? Just read these below-steps:

- Primitively, a set of unclassified data points can be reviewed as now there is no need for specifying the number of clusters. Then, you should select some arbitrary point like p and start computing the distance parameter ε for finding the neighborhood point.
- To proceed ahead with the clustering process, it is essential to find the minimum number of data points with which a densely-populated cluster can be formed. And that number can be denoted by variable minPts. Here, the process may stop if the new data point identified is greater than minPts.
- Keep on updating the values of ε and the current data point till the clusters of different densities are segmented well even better than DBSCAN.

Last Updated : 04 Jan, 2022 Like Article Save Article

#### Which clustering algorithm is most accurate?

K-means clustering algorithm – K-means clustering is the most commonly used clustering algorithm. It’s a centroid-based algorithm and the simplest unsupervised learning algorithm. This algorithm tries to minimize the variance of data points within a cluster.

It’s also how most people are introduced to unsupervised machine learning. K-means is best used on smaller data sets because it iterates over all of the data points. That means it’ll take more time to classify data points if there are a large amount of them in the data set. Since this is how k-means clusters data points, it doesn’t scale well.

Implementation: from numpy import unique from numpy import where from matplotlib import pyplot from sklearn.datasets import make_classification from sklearn.cluster import KMeans # initialize the data set we’ll work with training_data, _ = make_classification( n_samples=1000, n_features=2, n_informative=2, n_redundant=0, n_clusters_per_class=1, random_state=4 ) # define the model kmeans_model = KMeans(n_clusters=2) # assign each data point to a cluster dbscan_result = dbscan_model.fit_predict(training_data) # get all of the unique clusters dbscan_clusters = unique(dbscan_result) # plot the DBSCAN clusters for dbscan_cluster in dbscan_clusters: # get data points that fall in this cluster index = where(dbscan_result == dbscan_clusters) # make the plot pyplot.scatter(training_data, training_data) # show the DBSCAN plot pyplot.show()

#### What are the disadvantages of cure algorithm?

Disadvantage CURE fails to explain the inter-connectivity of objects in clusters. Model Static Static Sensitivity to outliers More sensitive Handles outliers effectively Time Complexity O(tKn) O(n2log n) Shapes Supports spherical shapes Supports non-spherical shapes, densities.

### How cure is different from chameleon?

Hierarchical Clustering: CURE and Chameleon Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters. The endpoint is a set of clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other.

- Chameleon is a hierarchical clustering algorithm that uses dynamic modeling to decide the similarity among pairs of clusters.
- It was changed based on the observed weaknesses of two hierarchical clustering algorithms such as ROCK and CURE.
- ROCK and related designs emphasize cluster interconnectivity while neglecting data regarding cluster proximity.

CURE and related design consider cluster proximity yet neglect cluster interconnectivity. In Chameleon, cluster similarity is assessed depending on how well-connected objects are inside a cluster and on the proximity of clusters. Especially, two clusters are combined if their interconnectivity is high and they are close together.

It does not base on a static, user-supplied model and can automatically adapt to the internal features of the clusters being combined. The merge process supports the discovery of natural and homogeneous clusters and is used for all types of data considering a similarity function can be defined. Chameleon needs the k-nearest-neighbor graph technique to make a sparse graph, where each vertex of the graph defines a data object, and there exists an edge among two vertices (objects) if one object is between the k-most-similar objects of the other.

The edges are weighted to reflect the similarity among objects. Chameleon uses a graph partitioning algorithm to partition the k-nearest-neighbor graph into a large number of relatively small subclusters. It can use an agglomerative hierarchical clustering algorithm that repeatedly merges subclusters based on their similarity.

- It can determine the pairs of most similar subclusters, it takes into account both the interconnectivity as well as the closeness of the clusters.
- The k-nearest-neighbor graph captures the approach of neighborhood dynamically: the neighborhood radius of an object is decided by the density of the region in which the object resides.

In a dense area, the neighborhood is represented narrowly. In a sparse region, it is represented more widely. This influence results in more natural clusters, in comparison with density-based methods like DBSCAN that instead use a worldwide neighborhood.

Furthermore, the density of the region is recorded as the weight of the edges. Especially, the edges of a dense region tend to weigh more than that of a sparse region. The graph-partitioning algorithm partitions the k-nearest-neighbor graph such that it makes smaller the edge cut. That is, cluster C is subdivided into sub-clusters Ciand Cj to minimize the weight of the edges that can be cut should C be bisected into Ci and Cj,

Edge cut is indicated EC (Ci, Cj )and determines the absolute interconnectivity between cluster Ci and Cj.

CURE Clustering | DBSCAN Clustering |

CURE Clustering stands for Clustering Using Representatives Clustering. | DBSCAN Clustering stands for Density Based Spatial Clustering of Applications with Noise Clustering. |

It is a hierarchial based clustering technique. | It is a density based clustering technique. |

Noise handling in CURE clustering is not efficient. | Noise handling in DBSCAN clustering is efficient. |

Algorithm :
Draw a random sample. Partition the random sample. Partially cluster the partition. Outliers are identified and eliminated. The partial clusters obtained are clubbed into clustered. Label the result on storage. |
Algorithm:
All the data sample points are labelled as core points, border points or noise points. The noise points are eliminated. All the core points are connected which lie under the vicinity of Eps of each other. The core points which are connected to each other are grouped into a separate cluster. Border points are assigned to each clusters. |

It can take care of high dimensional datasets. | It does not work properly for high dimensional datasets. |

Varying densities of the data points doesn’t matter in CURE clustering algorithm. | It does not work properly when the data points have varying densities |

Hierarchical Clustering: CURE and Chameleon

### What is the most efficient algorithm?

The most efficient algorithm is one that takes the least amount of execution time and memory usage possible while still yielding a correct answer.

## What are the advantages of cure algorithm?

CURE can handle large databases efficiently. CURE can effectively detect proper shape of the cluster with the help of scattered representative point and centroid shrinking. CURE can effectively remove outlier.

### What is cure and chameleon?

Hierarchical Clustering: CURE and Chameleon Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters. The endpoint is a set of clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other.

- Chameleon is a hierarchical clustering algorithm that uses dynamic modeling to decide the similarity among pairs of clusters.
- It was changed based on the observed weaknesses of two hierarchical clustering algorithms such as ROCK and CURE.
- ROCK and related designs emphasize cluster interconnectivity while neglecting data regarding cluster proximity.

CURE and related design consider cluster proximity yet neglect cluster interconnectivity. In Chameleon, cluster similarity is assessed depending on how well-connected objects are inside a cluster and on the proximity of clusters. Especially, two clusters are combined if their interconnectivity is high and they are close together.

It does not base on a static, user-supplied model and can automatically adapt to the internal features of the clusters being combined. The merge process supports the discovery of natural and homogeneous clusters and is used for all types of data considering a similarity function can be defined. Chameleon needs the k-nearest-neighbor graph technique to make a sparse graph, where each vertex of the graph defines a data object, and there exists an edge among two vertices (objects) if one object is between the k-most-similar objects of the other.

The edges are weighted to reflect the similarity among objects. Chameleon uses a graph partitioning algorithm to partition the k-nearest-neighbor graph into a large number of relatively small subclusters. It can use an agglomerative hierarchical clustering algorithm that repeatedly merges subclusters based on their similarity.

It can determine the pairs of most similar subclusters, it takes into account both the interconnectivity as well as the closeness of the clusters. The k-nearest-neighbor graph captures the approach of neighborhood dynamically: the neighborhood radius of an object is decided by the density of the region in which the object resides.

In a dense area, the neighborhood is represented narrowly. In a sparse region, it is represented more widely. This influence results in more natural clusters, in comparison with density-based methods like DBSCAN that instead use a worldwide neighborhood.

Furthermore, the density of the region is recorded as the weight of the edges. Especially, the edges of a dense region tend to weigh more than that of a sparse region. The graph-partitioning algorithm partitions the k-nearest-neighbor graph such that it makes smaller the edge cut. That is, cluster C is subdivided into sub-clusters Ciand Cj to minimize the weight of the edges that can be cut should C be bisected into Ci and Cj,

Edge cut is indicated EC (Ci, Cj )and determines the absolute interconnectivity between cluster Ci and Cj.

CURE Clustering | DBSCAN Clustering |

CURE Clustering stands for Clustering Using Representatives Clustering. | DBSCAN Clustering stands for Density Based Spatial Clustering of Applications with Noise Clustering. |

It is a hierarchial based clustering technique. | It is a density based clustering technique. |

Noise handling in CURE clustering is not efficient. | Noise handling in DBSCAN clustering is efficient. |

Algorithm :
Draw a random sample. Partition the random sample. Partially cluster the partition. Outliers are identified and eliminated. The partial clusters obtained are clubbed into clustered. Label the result on storage. |
Algorithm:
All the data sample points are labelled as core points, border points or noise points. The noise points are eliminated. All the core points are connected which lie under the vicinity of Eps of each other. The core points which are connected to each other are grouped into a separate cluster. Border points are assigned to each clusters. |

It can take care of high dimensional datasets. | It does not work properly for high dimensional datasets. |

Varying densities of the data points doesn’t matter in CURE clustering algorithm. | It does not work properly when the data points have varying densities |

Hierarchical Clustering: CURE and Chameleon

#### How does this algorithm work?

Much of what we do in our day-to-day lives comprises an algorithm: a sequence of step-by-step instructions geared to garner results. In the digital sphere, algorithms are everywhere. They’re the key component of any computer program, built into operating systems to ensure our devices adhere to the correct commands and deliver the right results on request.

An algorithm is a coded formula written into software that, when triggered, prompts the tech to take relevant action to solve a problem. Computer algorithms work via input and output. When data is entered, the system analyses the information given and executes the correct commands to produce the desired result.

For example, a search algorithm responds to our search query by working to retrieve the relevant information stored within the data structure. There are three constructs to an algorithm.

Linear sequence: The algorithm progresses through tasks or statements, one after the other. Conditional: The algorithm makes a decision between two courses of action, based on the conditions set, i.e. if X is equal to 10 then do Y. Loop: The algorithm is made up of a sequence of statements that are repeated a number of times.

The purpose of any algorithm is to eliminate human error and to arrive at the best solution, time and time again, as quickly and efficiently as possible. Useful for tech users, but essential for data scientists, developers, analysts and statisticians, whose work relies on the extraction, organisation and application of complex data sets.

### What is the index of the cure?

How Does the Cure Index Improve Powder Coating Quality Control? – PosiSoft Desktop software can now calculate the Cure Index from PosiTest OTL Oven Temperature Logger measurement data. Cure Index allows the user to quickly determine whether each measurement point has reached a sufficient ‘time at temperature’ to fully cure the coating.

- 7 minutes at 200 °C (392 °F)
- 10 minutes at 193 °C (379 °F)
- 14 minutes at 182 °C (360 °F)
- 18 minutes at 173 °C (343 °F)
- 23 minutes at 160 °C (320 °F)
- 27 minutes at 149 °C (300 °F)

It is important to note that the specified temperatures are substrate (part) temperatures, not air temperatures. In practice, it is impossible to instantly heat the substrate to a selected temperature, hold that temperature, and then instantly cool the part back to room temperature.

- While the part is warming and cooling, it spends time above the activation temperature (minimum cure temperature) but below the target temperature
- While transiting the oven, the part often spends time above the target temperature

It is valuable to go beyond measuring the elapsed time at a selected temperature, to account for the curing that happens when the part is above and below the target temperature.