Found 426 Questions for Data Mining

What are the methods of outlier detection?

Ginni
Updated on 18-Feb-2022 10:30:11
There are various methods of outlier detection is as follows −Supervised Methods − Supervised methods model data normality and abnormality. Domain professionals tests and label a sample of the basic data. Outlier detection can be modeled as a classification issue. The service is to understand a classifier that can identify outliers.The sample can be used for training and testing. In various applications, the professionals can label only the normal objects, and several objects not connecting the model of normal objects are documented as outliers. There are different methods model the outliers and consider objects not connecting the model of outliers ... Read More

What are the challenges of Outlier detection?

Ginni
Updated on 18-Feb-2022 10:28:33
An outlier is a data object that deviates essentially from the rest of the objects, as if it were produced by a different structure. For ease of presentation, it can define data objects that are not outliers as “normal” or expected information. Similarly, it can define outliers as “abnormal” data.Outliers are data components that cannot be combined in a given class or cluster. These are the data objects which have several behaviour from the general behaviour of different data objects. The analysis of this kind of data can be important to mine the knowledge.There are various challenges of outlier detection ... Read More

What are the types of Outliers in data mining?

Ginni
Updated on 18-Feb-2022 10:26:01
There are various types of outliers in data mining are as follows −Global Outliers − In a given data set, a data object is a global outlier if it deviates essentially from the rest of the information set. Global outliers are known as point anomalies, and are the easiest type of outliers. Most outlier detection methods are aimed at discovering global outliers.It can identify global outliers, an important issue is to discover an appropriate measurement of deviation concerning the application in question. There are several measurements are proposed, and, depends on these, outlier detection approaches are partitioned into multiple categories.Global ... Read More

What are Outliers?

Ginni
Updated on 18-Feb-2022 10:24:01
An outlier is a data object that diverge essentially from the rest of the objects, as if it were produced by a several mechanism. For ease of presentation, it can define data objects that are not outliers as “normal” or expected information. Usually, it can define outliers as “abnormal” data.Outliers are data components that cannot be combined in a given class or cluster. These are the data objects which have several behaviour from the usual behaviour of different data objects. The analysis of this kind of data can be important to mine the knowledge.Outliers are different from noisy information. Noise ... Read More

What are the methods for Clustering with Constraints?

Ginni
Updated on 18-Feb-2022 10:19:33
There are various techniques are required to handle specific constraints. The general principles of handling hard and soft constraints which are as follows −Handling Hard Constraints − A general methods for handling difficult constraints is to strictly regard the constraints in the cluster assignment procedure. Given a data set and a group of constraints on examples (i.e., must-link or cannot-link constraints), how can we develop the k-means approach to satisfy such constraints? The COP-kmeans algorithm works as follows −Generate super instances for must-link constraints − It can calculate the transitive closure of the must-link constraints. Therefore, all must-link constraints are ... Read More

What are the Categorization of Constraints in data mining?

Ginni
Updated on 18-Feb-2022 10:02:36
Constraint-based algorithms need constraints to decrease the search area in the frequent itemset generation phase (the association rule creating step is exact to that of exhaustive algorithms).The importance of constraints is well-defined and they make only association rules that are interesting to customers. The method is quite trivial and the rules area is decreased whereby remaining rules use the constraints.There are three types of constraints which are as follows −Constraints on instances − A constraint on instances defines how a pair or a set of instances must be grouped in the cluster analysis. There are two types of constraints from ... Read More

How can we measure the similarity or distance between two vertices in a graph?

Ginni
Updated on 18-Feb-2022 10:09:11
There are two types of measures such as geodesic distance and distance based on random walk.Geodesic Distance − A simple measure of the distance among two vertices in a graph is the shortest route among the vertices. Usually, the geodesic distance among two vertices is the length in terms of the multiple edges of the shortest path among the vertices. For two vertices that are not linked in a graph, the geodesic distance is represented as infinite.By utilizing geodesic distance, it can represent various useful measurements for graph analysis and clustering. Given a graph G = (V, E), where V ... Read More

What are the applications of Bipartite graphs?

Ginni
Updated on 18-Feb-2022 07:31:57
In a bipartite graph, vertices can be splitted into two disjoint sets so that each edge connected a vertex in one set to a vertex in the multiple set. For the AllElectronics user purchase data, one set of vertices defines users, with one users per vertex. The multiple set defines products, with one product per vertex. An edge links a user to a product, defining the purchase of the product by the user.There are various applications of Bipartite graphs which is as follows −Web search engines − In web search engines, search logs are archived to data user queries and ... Read More

How can we find subspace clusters from high-dimensional data?

Ginni
Updated on 18-Feb-2022 07:30:18
There are several methods have been categorized into three major groups including subspace search techniques, correlation-based clustering techniques, and biclustering techniques.Subspace Search Technique − A subspace search method searches several subspaces for clusters. Therefore, a cluster is a subset of objects that are the same as each other in a subspace. The similarity is acquired by conventional measures including distance or density.For instance, the CLIQUE algorithm is a subspace clustering technique. It can specify the subspaces and the clusters in those subspaces in a dimensionality-increasing series and uses antimonotonicity to prune subspaces in which no cluster can continue. A bigger ... Read More

What is Active Learning?

Ginni
Updated on 18-Feb-2022 07:25:56
Active learning is a repetitive type of supervised learning that is relevant for situations where data are sufficient, but the class labels are scarce or costly to acquire. The learning algorithm is active in that it can carefully query a user (e.g., a person oracle) for labels. The multiple tuples used to understand a concept this method is smaller than the number needed in typical supervised learning.It is used to maintain costs down, the active learner objective to achieve high accuracy utilizing as few labeled examples as possible. Let D be all of data under consideration. There are several methods ... Read More
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