Discovered Knowledge Is Visually Represented Computer Science Essay

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Date selection and data transformation can also be combined where the consolidation of the date is the result of the selection or as for the case of data warehouses the selection is done on transformed data. The KDD is an recurrence process. Once the discovered knowledge is presented to the user the evaluation measure can be enhanced the mining can be further refined new data can be selected or further transformed or new data sources can be included in order to get different more appropriate results. Data mining derives its name from the similarities between searching for precious information in a large database and mining. Both imply either sifting through a large amount of material the material to exactly pinpoint where the values reside. Other similar terms referring to data mining are data dredging knowledge extraction and pattern sighting.


In principle data mining not specific to one type of media or data. Data mining should be applicable to any kind of information depository. However algorithms and approaches may change when applied to different types of data. Indeed the challenges presented by different types of data vary appreciably. Data mining is being put into use and studied for databases including relational databases object-relational databases and object-oriented databases data warehouse transactional databases unstructured and semi structured repositories such as the World Wide Web advanced databases such as spatial database multimedia database time-series databases and textual databases and even flat files. Here are some examples in more details.

Flat Files

Flat filed are actually the most common data source for data mining algorithms, especially at the research level. Flat files are simple data files in text or binary format with a structure known by the data mining algorithm to be applied. The data in these filed can be transactions, time-series, scientific measurement, etc.

Relational Databases

Briefly, a relational database consists of a set of table containing either values of entity attributes, or values of attributes from entity relationships. Tables have columns and rows, where columns represent attributes and rows represent tuples. A tuple in a relational table corresponds to either an object or a relationship between objects and is identified by a set of attribute values representing a unique key.

Data Warehouse

A data warehouse as a storehouse is a repository of data collected from multiple data sources (often heterogeneous) and is intended to be used as a whole under the same unified schema. A DW gives the option to analyze data from different sources under the same roof. Let us suppose that Our Video Store becomes a franchise in North America. Many Video stores belonging to Our Video store company may have different databases and different structures. If the executive of the company wants to access the data from all stores for strategic decision-making,, future direction, marketing, etc., it would be more appropriate to store all the data in one site with a homogeneous structure that allows interactive analysis. In other words, data from the different stores would be loaded, cleaned, transformed and integrated together. To facilitate decision making and multidimensional views, data warehouse are usually modeled by a multi dimensional data structure.

Transaction Databases

A transaction database is a set of records representing transactions, each with a time stamp, an identifier and a set of items. Associated with the transaction files could also be descriptive data for the items. Each record is a rental contract with a customer identifier, a date, and the list of times rented (i.e. video tapes, games, VCR, etc.). Since relational databases do not allow nested tables (i.e. as set as attributee value), transactions are usually stored in flat files or stored in two normalized transaction tables, one for the transactions and one for the transaction items. One typical data mining analysis on such data is the so-called market.

Multimedia Databases

Multimedia databases include video, images, audio and text media. They can be stored on extended object-relational or object-oriented databases, or simply on a file system. Multimedia is characterized by its high dimensionality, which makes data mining even more challenging. Data mining from multimedia repositories may require computer vision, computer graphics, image interpretation, and natural language processing methodologies.

Spatial Databases

Spatial database are databases that, in addition to usual data, store geographical information like maps, and global or regional positioning. Such spatial databases present new challenges to data mining algorithms.

Time-Series Database

Time-series databases contain time related dta such stock market data. These databases usually have a continuous flow of new data coming in, which sometimes causes the need for a challenging real time analysis. Data mining in such databases commonly includes the study of trends and correlations between evolutions of different variable, as well as the prediction of trends and movements of the variable in time.

World Wide Web

The World Wide Web is the most heterogeneous and dynamic repository available. A very large number of authors and publishers are continuously contributing to its growth and metamorphosis, and a massive number of users are accessing its resources daily. Data in the World Wide Web is organized in inter-connected documents. These documents can be text, audio, video, raw data, and even applications. Conceptually, the World Wide Web is comprised of three major components: The content of the Web, Which encompasses documents available; the structure of the Web, which covers the relationship between documents; and the usage of the web, describing how and when the resources are accessed. A fourth dimension can be added relating the dynamic nature or evolution of the documents. Data mining in the World Wide Web, or web mining, tries to address all these issues and is often divided into web content mining, web structure mining and web usage mining.


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 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 lists:


Data characterization is a summarization of general features of objects in a target class, and produces what is called characteristic rules. The data relevant to a user-specified class are normally retrieved by a database query and run through a summarization module to extract the essence of the data at different levels of abstractions. For example, one may want to characterize the Our Video Store customers who regularly rent more than 30 movies a year. With concept hierarchies on the attributes describing the target class, the attribute oriented induction method can be used, for example, to carry out data summarization. Note that with a data cube containing summarization of data, simple OLAP operations fit the purpose of data characterization.


Data discrimination produces what are called discriminate rules and is basically the comparison of the general features of objects between two classes referred to as the target class and the contrasting class. For example, one may want to compare the general characteristics of the customers who rented more than 30 movies in the last year with those whose rental account is lower than 5. The techniques used for data discrimination are very similar to the techniques used for data characterization with the exception that data discrimination results include comparative measures.

Association analysis

Association analysis is the discovery of what are commonly called association rules. It studies the frequency of items occurring together in transactional databases, and based on a threshold called support, identifies the frequent item sets. Another threshold, confidence, which is the conditional probability than an item appears in a transaction when another item appears, is used to pinpoint association rules. Association analysis is commonly used for market basket analysis. For example, it could be useful for the Our Video Store manager to know what movies are often rented together or if there is a relationship between renting a certain type of movies and buying popcorn or pop. The discovered association rules are of the form: P→Q [s,c], where P and Q are conjunctions of attribute value-pairs, and s (for support is the probability that P and Q appear together in a transaction and c (for confidence) is the conditional probability that appears in a transaction when P is present. For example, the hypothetic association rules:

Rent Type (X, "game") "+Age(X, "13-19") "+Buys(X, "pop") [s=2%, c=55%] would indicate that 2% of the transactions considered are of customers aged between 13 and 19 who are renting a game and buying a pop, and that there is a certainly of 55% that teenage customers who rent a game also buy pop.


Classification analysis is the organization of data in given classes. Also known as supervised classification, the classification uses given class labels to order the objects in the data collection. Classification approaches normally use a training set where all objects are already associated with known class labels. The classification algorithm learns from the training set and builds a model. The model is used to classify new objects. For example, after starting a credit policy, the Our Video Store managers could analyze the customers' behaviours vis-à-vis their credit, and label accordingly the customers who received credits with three possible labels "sale", "risky" and "very risky".


Prediction has attracted considerable attention given the potential implications of successful forecasting in a business context. There are two major types of predictions: one can either try to predict some unavailable data values or pending trends, or predict a class label for some data. The latter is tied to classification. Once a classification model is built based on a training set, the class label of an object and the attribute values of the classes. Prediction is however more often referred to the forecast of missing numerical values, or increase decrease trends in time related data. The major idea is to use a large number of past values to consider probable future values.


Similar to classification, clustering is the organization of data in classes. However, unlike classification, in clustering, class labels are unknown and it is up to the clustering algorithm to discover acceptable classes. Clustering is also called unsupervised classification, because the classification is not dictated by given class labels. There are many clustering approaches all based on the principle of maximizing the similarity between objects in a same class (intra-class similarity) and minimizing the similarity between objects of different classes (inter-class similarity).

Outlier analysis

Outliers are data elements that cannot be grouped in a given class or cluster. Also known as exceptions or surprises, they are often very important to identify. While outliers can be considered noise and discarded in some applications, they can reveal important knowledge in other domains, and thus can be very significant and their analysis valuable.

Evolution and deviation analysis

Evolution and deviation analysis pertain to the study of time related data that changes in time. Evolution analysis models evolutionary trends in data, which consent to characterizing, comparing, classifying or clustering of time related data. Deviation analysis, on the other hand, considers differences between measured values and expected values, and attempts to find the cause of the deviations from the anticipated values. 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 certainly, or on some subjective 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 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:

Classification according to the type of data source mined

This classification categorizes data mining systems according to the type of data handled such as spatial data, multimedia data, time-series data, text data, World Wide Web etc.

Classification according to the data model drawn on

This classification categorized data mining systems based on the data model involved such as relational database, object-oriented database, data warehouse, transactional etc.

Classification according to the kind of knowledge discovered

This classification categorizes data mining systems based on the kind of knowledge discovered or data mining functionalities, such as characterization, discrimination, association, classification, clustering, etc. Some systems tend to be comprehensive systems offering several data mining functionalities together.

Classification according to mining techniques used.

Data mining system employ and provide different techniques. This classification categorized data mining system according to the data analysis approach used such as machine learning, neural networks, genetic algorithms, statistics, visualization, database oriented or data warehouse-oriented, etc. The classification can also take into account the degree of user interaction involved in the data mining process such as query-driven systems, interactive exploratory systems, or autonomous systems.


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 issued are not exclusive and are not ordered in any way.

Security and social issues

Security is an important issue with any data collection that is shared and/or is intended to be used for strategic decision-making. In addition, when data is collected for customer profiling, user behavior understanding, correlating personal data with other information, etc., large amounts of sensitive and private information about individuals or companies is gathered and stored. 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 users. Good data visualization eases the interpretation of data mining results, as well as helps users better understand their needs. Many data exploratory tasks 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. The major issues related to user interfaces and visualization are "screen real-estate", information rendering, and interaction. Interactivity with the data and data 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 need (when known), the assessment of the knowledge discovered, the exploitation of background knowledge and metadata, the control and handling of noise in data, etc. are all examples that can dictate mining methodology choices. For instance, it is often desirable to have different data mining methods available since different approaches many 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., which may complicate, if not obscure, the analysis process and in many cases compromise the accuracy of the results. 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 is 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 dimension 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. This raises the issues of scalability and efficiency of the data mining methods when processing considerably large data. Algorithms with exponential and even medium-order polynomial complexity cannot be of practical use for data mining. Linear algorithms are usually the norm. In same theme, sampling can be used for mining instead of the whole dataset. However, concerns such as completeness and choice of samples may arise. Other topics in the issue of performance are incremental updating, and parallel programming. There is no doubt that parallelism can help solve the size problem If the dataset can be subdivided and the results can be merged later. Incremental updating is important for merging results from parallel mining, or updating data mining results when new data becomes available without having to re-analyze the complete dataset.

Date source issues

There many issues related to the data source, some are practical such as the diversity of data types, while other are philosophical like the data glut problem. We certainly have an excess of data since we already have more data than we can handle and we are still collecting data at an even higher rate. If the spread of database management systems has helped increase the gathering of information, the advent of data mining is certainly encouraging more data harvesting. The current practice is to collect as much data as possible now and process, or try to process it, later. The concern is whether we are collecting the right data at the appropriate amount, whether we known what we want to do with it, and whether we distinguish between what data is important and what data is insignificant. Regarding the practical issues related to data sources, there is the subject of heterogeneous databases and the focus on diverse complex data types. We are storing different types of data in a variety of repositories. It is difficult to expect a data mining system to effectively and efficiently achieve good mining results on all kinds of data and sources. Different kinds of data and sources many require distinct algorithms and methodologies. Currently, there is a focus on relational databases and data warehouse, but other approaches need to be pioneered for other specific complex data types. A versatile data mining tool, for all sorts of data, may not be realistic. Moreover, the proliferation of heterogeneous data source, at structural and semantic levels, poses important challenges not only to the database community but also to the data mining community.