A sequence is an ordered list of events. Sequences can be divided into three groups, based on the features of the events they define as follows −Similarity Search in Time-Series DataA time-series data set includes sequences of integer values acquired over repeated computation of time. The values are generally measured at same time intervals (such as each minute, hour, or day).Time-series databases are famous in several applications including stock market analysis, economic and sales predicting, budgetary analysis, utility studies, inventory studies, revenue projections, workload projections, and process and quality service. They are beneficial for studying natural phenomena, mathematical and engineering ... Read More

There are various challenges of outlier detection in high-dimensional data are as follows −Interpretation of outliers − They must be able to not only identify outliers, but also support an interpretation of the outliers. Because several features (or dimensions) are contained in a high-dimensional data set, identifying outliers without supporting some interpretation as to why they are outliers is not very helpful.The interpretation of outliers can appear from definite subspaces that manifest the outliers or an assessment concerning the “outlierness” of the objects. Such interpretation can support users to learn the possible meaning and importance of the outliers.Data sparsity − ... Read More

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

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

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

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

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

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

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

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