Data mining on what kind of data

This technique aims to use transaction data, and then identify similar trends, patterns, and events in it over a period of time. The historical sales data can be used to discover items that buyers bought together at different times of the year. Businesses can use lucrative deals and discounts to push through this recommendation. Below are some most useful data mining applications lets know more about them. Data mining has the potential to transform the healthcare system completely.

It can be used to identify best practices based on data and analytics, which can help healthcare facilities to reduce costs and improve patient outcomes. Data mining, along with machine learning, statistics, data visualization, and other techniques can be used to make a difference.

It can come in handy when forecasting patients of different categories. This will help patients to receive intensive care when and where they want it. Data mining can also help healthcare insurers to identify fraudulent activities. Use of data mining in education is still in its nascent phase. It aims to develop techniques that can use data coming out of education environments for knowledge exploration.

The purposes that these techniques are expected to serve include studying how educational support impacts students, supporting the future-leaning needs of students, and promoting the science of learning amongst others. Educational institutions can use these techniques to not only predict how students are going to do in examinations but also make accurate decisions.

With this knowledge, these institutions can focus more on their teaching pedagogy. This is a modelling technique that uses hypothesis as a basis. Retailers can use this technique to understand the buying habits of their customers.

Retailers can use this information to make changes in the layout of their store and to make shopping a lot easier and less time consuming for customers. CRM involves acquiring and keeping customers, improving loyalty, and employing customer-centric strategies. Every business needs customer data to analyze it and use the findings in a way that they can build a long-lasting relationship with their customers.

Data mining can help them do that. A manufacturing company relies a lot on the data or information available to it. Data mining can help these companies in identifying patterns in processes that are too complex for a human mind to understand. They can identify the relationships that exist between different system-level designing elements, including customer data needs, architecture, and portfolio of products.

Data mining can also prove useful in forecasting the overall time required for product development, the cost involved in the process, and the expectations companies can have from the final product. The banking system has been witnessing the generation of massive amounts of data from the time it underwent digitalization. Bankers can use data mining techniques to solve the baking and financial problems that businesses face by finding out correlations and trends in market costs and business information.

This job is too difficult without data mining as the volume of data that they are dealing with is too large. Managers in the banking and financial sectors can use this information to acquire, retain, and maintain a customer.

Learn more: Association Rule Mining. Fraudulent activities cost businesses billions of dollars every year. Methods that are usually used for detecting frauds are too complex and time-consuming. Data mining provides a simple alternative. Every ideal fraud detection system needs to protect user data in all circumstances. A method is supervised to collect data, and then this data is categorized into fraudulent or non-fraudulent data.

This data is used in training a model that identifies every document as fraudulent or non-fraudulent. Known as one of the fundamental data mining techniques , it generally comprises tracking data patterns to derive business conclusions. For an organization, it could mean anything from identifying sales upsurge or tapping newer demographics. To derive relevant metadata, the classification technique in data mining helps in differentiating data into separate classes:.

Depending on the type of data handled like text-based data, multimedia data, spatial data, time-series data, etc. Any data set that is based on the object-oriented database, relational database, etc. Here the data sets are differentiated based on the approach taken like Machine Learning, Algorithms, Statistics, Database or data warehouse, etc. The datasets are used to differentiate based on query-driven systems, autonomous systems. Otherwise known as relation technique, the data is identified based on the relationship between the values in the same transaction.

It is especially handy for organizations trying to spot trends into purchases or product preferences. If a data item is identified that does not match up to a precedent behavior, it is an outlier or an exception. This method digs deep into the process of the creation of such exceptions and backs it with critical information. Generally, anomalies can be aloof in its origin, but it also comes with the possibility of finding out a focus area.

Data mining algorithms using relational databases can be more versatile than data mining algorithms specifically written for flat files, since they can take advantage of the structure inherent to relational databases. While data mining can benefit from SQL for data selection, transformation and consolidation, it goes beyond what SQL could provide, such as predicting, comparing, detecting deviations, etc.

The figure shows summarized rentals grouped by film categories, then a cross table of summarized rentals by film categories and time in quarters. A cube contains cells that store values of some aggregate measures in this case rental counts , and special cells that store summations along dimensions.

Each dimension of the data cube contains a hierarchy of values for one attribute. Because of their structure, the pre-computed summarized data they contain and the hierarchical attribute values of their dimensions, data cubes are well suited for fast interactive querying and analysis of data at different conceptual levels, known as On-Line Analytical Processing OLAP.

OLAP operations allow the navigation of data at different levels of abstraction, such as drill-down, roll-up, slice, dice, etc.

Figure 1. The kinds of patterns that can be discovered depend upon the data mining tasks employed. By and large, there are two types of data mining tasks: descriptive data mining tasks that describe the general properties of the existing data, and predictive data mining tasks that attempt to do predictions based on inference on available data.

The data mining functionalities and the variety of knowledge they discover are briefly presented in the following list:. It is common that users do not have a clear idea of the kind of patterns they can discover or need to discover from the data at hand. It is therefore important to have a versatile and inclusive data mining system that allows the discovery of different kinds of knowledge and at different levels of abstraction.

This also makes interactivity an important attribute of a data mining system. Data mining allows the discovery of knowledge potentially useful and unknown. Whether the knowledge discovered is new, useful or interesting, is very subjective and depends upon the application and the user.

It is certain that data mining can generate, or discover, a very large number of patterns or rules. In some cases the number of rules can reach the millions. One can even think of a meta-mining phase to mine the oversized data mining results. To reduce the number of patterns or rules discovered that have a high probability to be non-interesting, one has to put a measurement on the patterns.

However, this raises the problem of completeness. The user would want to discover all rules or patterns, but only those that are interesting. The measurement of how interesting a discovery is, often called interestingness , can be based on quantifiable objective elements such as validity of the patterns when tested on new data with some degree of certainty , or on some subjective depictions such as understandability of the patterns, novelty of the patterns, or usefulness.

Discovered patterns can also be found interesting if they confirm or validate a hypothesis sought to be confirmed or unexpectedly contradict a common belief. This brings the issue of describing what is interesting to discover, such as meta-rule guided discovery that describes forms of rules before the discovery process, and interestingness refinement languages that interactively query the results for interesting patterns after the discovery phase.

Typically, measurements for interestingness are based on thresholds set by the user. These thresholds define the completeness of the patterns discovered. Identifying and measuring the interestingness of patterns and rules discovered, or to be discovered, is essential for the evaluation of the mined knowledge and the KDD process as a whole.

While some concrete measurements exist, assessing the interestingness of discovered knowledge is still an important research issue. There are many data mining systems available or being developed. Some are specialized systems dedicated to a given data source or are confined to limited data mining functionalities, other are more versatile and comprehensive. Data mining systems can be categorized according to various criteria among other classification are the following:.

Data mining algorithms embody techniques that have sometimes existed for many years, but have only lately been applied as reliable and scalable tools that time and again outperform older classical statistical methods. While data mining is still in its infancy, it is becoming a trend and ubiquitous. Before data mining develops into a conventional, mature and trusted discipline, many still pending issues have to be addressed. Some of these issues are addressed below.

Note that these issues are not exclusive and are not ordered in any way. In addition, when data is collected for customer profiling, user behaviour understanding, correlating personal data with other information, etc. This becomes controversial given the confidential nature of some of this data and the potential illegal access to the information. Moreover, data mining could disclose new implicit knowledge about individuals or groups that could be against privacy policies, especially if there is potential dissemination of discovered information.

Another issue that arises from this concern is the appropriate use of data mining. Due to the value of data, databases of all sorts of content are regularly sold, and because of the competitive advantage that can be attained from implicit knowledge discovered, some important information could be withheld, while other information could be widely distributed and used without control.

User interface issues : The knowledge discovered by data mining tools is useful as long as it is interesting, and above all understandable by the user. Good data visualization eases the interpretation of data mining results, as well as helps users better understand their needs. Many data exploratory analysis tasks are significantly facilitated by the ability to see data in an appropriate visual presentation.

There are many visualization ideas and proposals for effective data graphical presentation. However, there is still much research to accomplish in order to obtain good visualization tools for large datasets that could be used to display and manipulate mined knowledge. Interactivity with the data and data mining results is crucial since it provides means for the user to focus and refine the mining tasks, as well as to picture the discovered knowledge from different angles and at different conceptual levels.

Mining methodology issues : These issues pertain to the data mining approaches applied and their limitations. Topics such as versatility of the mining approaches, the diversity of data available, the dimensionality of the domain, the broad analysis needs when known , the assessment of the knowledge discovered, the exploitation of background knowledge and metadata, the control and handling of noise in data, etc.

For instance, it is often desirable to have different data mining methods available since different approaches may perform differently depending upon the data at hand. Moreover, different approaches may suit and solve user's needs differently. Most algorithms assume the data to be noise-free. This is of course a strong assumption. Most datasets contain exceptions, invalid or incomplete information, etc. As a consequence, data preprocessing data cleaning and transformation becomes vital. It is often seen as lost time, but data cleaning, as time-consuming and frustrating as it may be, is one of the most important phases in the knowledge discovery process.

Data mining techniques should be able to handle noise in data or incomplete information. More than the size of data, the size of the search space is even more decisive for data mining techniques. The size of the search space is often depending upon the number of dimensions in the domain space. The search space usually grows exponentially when the number of dimensions increases. This is known as the curse of dimensionality.

This "curse" affects so badly the performance of some data mining approaches that it is becoming one of the most urgent issues to solve. Performance issues : Many artificial intelligence and statistical methods exist for data analysis and interpretation. However, these methods were often not designed for the very large data sets data mining is dealing with today. Terabyte sizes are common. Choose your reason below and click on the Report button. This will alert our moderators to take action.

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Examples include geographic map databases, very large-scale integration VLSI or computed-aided design databases, and medical and satellite image databases. Spatial data may be represented in raster format, consisting of n -dimensional bit maps or pixel maps. For example, a 2-D satellite image may be represented as raster data, where each pixel registers the rainfall in a given area. Maps can be represented in vector format, where roads, bridges, buildings, and lakes are represented as unions or overlays of basic geometric constructs, such as points, lines, polygons, and the partitions and networks formed by these components.

Data mining may uncover patterns describing the characteristics of houses located near a specified kind of location, such as a park, for instance. A spatial database that stores spatial objects that change with time is called a spatiotemporal database, from which interesting information can be mined.

Text Databases and Multimedia Databases. Text databases are databases that contain word descriptions for objects. These word descriptions are usually not simple keywords but rather long sentences or paragraphs, such as product specifications, error or bug reports, warning messages, summary reports, notes, or other documents.

Text databases may be highly unstructured such as some Web pages on the WorldWideWeb. Text databases with highly regular structures typically can be implemented using relational database systems. Multimedia databases store image, audio, and video data. They are used in applications such as picture content-based retrieval, voice-mail systems, video-on-demand systems, the World Wide Web, and speech-based user interfaces that recognize spoken commands.

Multimedia databases must support large objects, because data objects such as video can require gigabytes of storage. Specialized storage and search techniques are also required. Because video and audio data require real-time retrieval at a steady and predetermined rate in order to avoid picture or sound gaps and system buffer overflows, such data are referred to as continuous-media data. Heterogeneous Databases and Legacy Databases. A heterogeneous database consists of a set of interconnected, autonomous component databases.

The components communicate in order to exchange information and answer queries. Objects in one component database may differ greatly from objects in other component databases, making it difficult to assimilate their semantics into the overall heterogeneous database. A legacy database is a group of heterogeneous databases that combines different kinds of data systems, such as relational or object-oriented databases, hierarchical databases, network databases, spreadsheets, multimedia databases, or file systems.

The heterogeneous databases in a legacy database may be connected by intra or inter-computer networks. Data Streams.