Database Articles - Page 37 of 546

Bitcoin mining defines the process of authenticating and inserting transactional data to the public ledger. The public ledge is called the blockchain because it includes a set of the block. Bitcoin is virtual money receiving some value, and its value is not static, it change according to time. There is no Bitcoin supervisory body that manage the Bitcoin transactions.Bitcoin was produced under the pseudonym (False name) Satoshi Nakamoto, who declared the creation, and it was performed as open-source program. An only end-to-end version of computer money can allow online costs to be sent directly from one person to another without ... Read More

The Cross Industry Standard Process for Data Mining (CRISP-DM) was recognized as an approach to further standardise the M&V methodology and allows more efficient estimation of energy savings. There are several applications of CRISP-DM which are as follows −Business Understanding − A biomedical manufacturing facility was selected as a case study to create the feasibility of the application of DM to help M&V. A quality understanding of the business under analysis was important to execute the results at the modelling and evaluation phase of the process. This was implemented by carrying out a process walk-through, learning process flow diagrams, and ... Read More

Statistical approaches are model-based approaches such as a model is produced for the data, and objects are computed concerning how well they fit the model. Most statistical approaches to outlier detection are depends on developing a probability distribution model and considering how Iikely objects are below that model.An outlier is an object that has a low probability concerning a probability distribution model of the data. A probability distribution model is produced from the data by computing the parameters of a user-defined distribution.If the data is considered to have a Gaussian distribution, therefore the mean and standard deviation of the basic ... Read More

In anomaly detection, the objective is to discover objects that are different from multiple objects. Often, anomalous objects are referred to as outliers, because on a scatter plot of the data, they lie far away from multiple data points. Anomaly detection is called a deviation detection, because anomalous objects have attribute values that deviate essentially from the expected or general attribute values, or as exception mining, because anomalies are exceptional in several sense.In the globe, human society, or the domain of data groups, most events and objects are, by representation, common area or reglar. But it can have a keen ... Read More

In anomaly detection, the objective is to discover objects that are different from multiple objects. Often, anomalous objects are referred to as outliers, because on a scatter plot of the data, they lie far away from multiple data points. Anomaly detection is called a deviation detection, because anomalous objects have attribute values that deviate essentially from the expected or general attribute values, or as exception mining, because anomalies are exceptional in several sense.There are various application of anomalies detection which are as follows −Fraud Detection − The buying behavior of someone who keep a credit card is different from that ... Read More

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. ... Read More

The m by m proximity matrix for m data points can be defines as a dense graph in which each node is linked to some others and the weight of the edge between some group of nodes follow their pairwise proximity. Although each object has some method of similarity to each other object, for most data sets, objects are hugely same to a small number of objects and weakly same to most other objects.This feature can be used to sparsify the proximity graph (matrix), by setting some low-similarity (high-dissimilarity) values to 0 before starting the actual clustering process. The sparsification ... Read More

SOM represents Self-Organizing Feature Map. It is a clustering and data visualization approaches depends on a neural network viewpoint. The objective of SOM is to discover a set of centroids (reference vectors in SOM terminology) and to create each object in the data set to the centroid that supports the best closeness of that object. In neural network methods, there is one neuron related to each centroid.As with incremental K-means, data objects are phased one at a time and the nearest centroid is refreshed. Unlike K-means, SOM imposes a topographic sequencing on the centroids and nearby centroids are also upgraded. ... Read More

In prototype-based clustering, a cluster is a group of objects in which some object is nearer to the prototype that represents the cluster than to the prototype of some other cluster. A simple prototype-based clustering algorithm that needs the centroid of the elements in a cluster as the prototype of the cluster.There are various approaches of Prototype-Based clustering which are as follows −Objects are enabled to belong to higher than one cluster. Furthermore, an object belongs to each cluster with some weight. Such a method addresses the fact that some objects are similarly close to multiple cluster prototypes.A cluster is ... Read More

There are various characteristics of clustering algorithms which are as follows −Order Dependence − For several algorithms, the feature and number of clusters produced can vary, perhaps dramatically, based on the order in which the data is processed. While it can seem desirable to prevent such algorithms, sometimes the order dependence is associatively minor or the algorithm can have several desirable features.Non-determinism − Clustering algorithms, including K-means, are not order-dependent, but they make several results for each run because they based on an initialization step that needed a random choice. Because the feature of the clusters can vary from one ... Read More