The telecommunication industry has quickly evolved from providing local and long-distance telephone services to providing several other comprehensive communication services, such as fax, pager, cellular phone, web messenger, images, e-mail, computer and Web data transmission, and several data traffic.The integration of telecommunication, computer network, the Internet, and several other means of communication and computing is also underway. Furthermore, with the deregulation of the telecommunication market in several countries and the development of new computer and communication technologies, the telecommunication industry is rapidly expanding and hugely competitive.This makes a huge demand for data mining in order to support understanding the business ... Read More

The retail industry is a major application area for data mining because it collects huge amounts of records on sales, users shopping history, goods transportation, consumption, and service. The quantity of data collected continues to expand promptly, especially because of the increasing ease, accessibility, and popularity of business conducted on the internet, or e-commerce.Today, multiple stores also have websites where users can create purchases online. Some businesses, including Amazon.com (www.amazon.com), exist solely online, without any brick-and-mortar (i.e., physical) store areas. Retail data support a rich source for data mining.Retail data mining can help identify user buying behaviors, find user shopping ... Read More

Financial data collected in the banking and financial market are relatively done, reliable, and of huge quality, which supports systematic data analysis and data mining. Therefore it can present a few typical cases which are as follows −Design and construction of data warehouses for multidimensional data analysis and data mining − Data warehouses are required to be constructed for banking and financial records. Multidimensional data analysis methods must be used to analyze the general features of such data.For instance, one can like to view the debt and revenue changes by month, by region, by sector, and by several elements, along ... Read More

There are several popular text retrievals indexing techniques such as inverted indices and signature files.Inverted Index − An inverted index is an index structure that maintains two hash indexed or B+-tree indexed tables: document_table and term_table, where document_table consists of a set of document records, each including two fields: doc_id and posting_list, where posting_list is a list of methods (or pointers to methods) that appears in the document, arranged according to some relevance measure.term_table includes a set of term records, each including two fields: term_id and posting_list, where posting_list specifies a list of records identifiers in which the term occurs.It ... Read More

Text retrieval is the process of transforming unstructured text into a structured format to identify meaningful patterns and new insights. By using advanced analytical techniques, including Naïve Bayes, Support Vector Machines (SVM), and other deep learning algorithms, organizations are able to explore and find hidden relationships inside their unstructured data. There are two methods of text retrieval which are as follows −Document Selection − In document selection methods, the query is regarded as defining constraint for choosing relevant documents. A general approach of this category is the Boolean retrieval model, in which a document is defined by a set of ... Read More

Information retrieval (IR) is a field that has been developing in parallel with database systems for many years. Unlike the field of database systems, which has targeted query and transaction processing of structured data, information retrieval is concerned with the organization and retrieval of data from multiple text-based documents.Since information retrieval and database systems each handle different kinds of data, some database system problems are usually not present in information retrieval systems, such as concurrency control, recovery, transaction management, and update. There are some common information retrieval problems that are usually not encountered in traditional database systems, such as unstructured ... Read More

There are several challenging issues regarding the construction and utilization of spatial data warehouses. The first challenge is the unification of spatial information from heterogeneous sources and systems. Spatial data are usually stored in different industry firms and government agencies using various data formats.Data formats are not only structure-specific (e.g., raster- vs. vector-based spatial data, object-oriented vs. relational models, different spatial storage and indexing structures), but also vendor-specific (e.g., ESRI, MapInfo, Intergraph). There has been huge work on the unification and exchange of heterogeneous spatial data, which has paved the way for spatial data integration and spatial data warehouse construction.The ... Read More

A spatial database saves a huge amount of space-related data, including maps, preprocessed remote sensing or medical imaging records, and VLSI chip design data. Spatial databases have several features that distinguish them from relational databases. They carry topological and/or distance information, usually organized by sophisticated, multidimensional spatial indexing structures that are accessed by spatial data access methods and often require spatial reasoning, geometric computation, and spatial knowledge representation techniques.Spatial data mining refers to the extraction of knowledge, spatial relationships, or other interesting patterns not explicitly stored in spatial databases. Such mining demands the unification of data mining with spatial database ... Read More

An object identifier can be generalized as follows. First, the object identifier is generalized to the identifier of the lowest subclass to which the object belongs. The identifier of this subclass can then, in turn, be generalized to a higher level class/subclass identifier by climbing up the class/subclass hierarchy. Similarly, a class or a subclass can be generalized to its corresponding superclass (es) by climbing up its associated class/subclass hierarchy.Because object-oriented databases are organized into class/subclass hierarchies, some attributes or methods of an object class are not explicitly specified in the class but are inherited from higher-level classes of the ... Read More

Multi-relational clustering is the process of partitioning data objects into a set of clusters based on their similarity, utilizing information in multiple relations. In this section, it can introduce CrossClus (Cross-relational Clustering with user guidance), an algorithm for multi-relational clustering that explores how to utilize user guidance in clustering and tuple ID propagation to avoid physical joins.There is one major challenge in multi-relational clustering is that there are too many attributes in different relations, and usually, only a small portion of them are relevant to a specific clustering task.Consider the computer science department database. It can order to cluster students, ... Read More