All day, every day, companies are now collecting enormous amounts of data about their products, customers or by simply using their information systems. Such increased availability of data raises one important question: how to use the gathered data to generate meaningful information? What is important is to connect the data and discover patterns. There are several buzzwords \/ terms you may have already heard related to this approach, such as Data Mining, Multivariate statistics, Machine Learning and Artificial Intelligence. Sometimes it is hard to tell the difference between these various terms.<\/p>\n\n\n\n
1. Data Mining<\/a><\/p>\n\n\n\n 2. Artificial Intelligence<\/a><\/p>\n\n\n\n 3. Machine Learning<\/a><\/p>\n\n\n\n 4. Deep Learning<\/a><\/p>\n\n\n\n 5. Cognitive Computing<\/a><\/p>\n\n\n\n In this post, we will explain the differences in an understandable way and present the different methods associated with \u201cdigging for data\u201d \u2013 the \u201cGold of the 21st Century\u201d.<\/strong><\/a><\/p>\n\n\n\n Data Mining<\/strong> is the computing process of discovering patterns in large data sets<\/a> involving methods at the intersection of machine learning, statistics, and database systems. (Data Mining Curriculum\u201d<\/a>. ACM<\/a> SIGKDD<\/a>. 30\/04\/2006. Retrieved 27\/01\/2014.)<\/p>\n\n\n\n The aim of Data Mining is to detect correlations in data that are of interest to and useful to decision-makers. They may be used for better decision making, to address issues that may have been solved by suboptimal methods in the past. The use of statistical significance is certainly not sufficient for the operationalization of the interest and usefulness of statements.<\/p>\n\n\n\n For this reason, integrated methods and methods of artificial intelligence, pattern recognition, machine learning, as well as models from the respective field of application, are used in Data Mining. In addition, methods of (multivariate) statistics<\/strong> are used. In this case, coherent observations are used as a basis for several features, along with procedures for detecting and examining structures. [1]<\/a><\/p>\n\n\n\n In contrast to classical approaches used in the field of statistics, data mining not only extends to the examination of manually established hypotheses, but mainly involves the automatic<\/strong> generation of new hypotheses. [2]<\/a><\/p>\n\n\n\n The basic tasks involved in data mining are:<\/p>\n\n\n\n The following are pre-requisites for the application of data mining techniques:<\/p>\n\n\n\n Data Mining projects can be divided into several phases. The Cross Industry Standard Process for Data Mining, developed by an EU project between 1996 and 1999, sets out a six-phase cross-industry standard for Data Mining:<\/p>\n\n\n\n Data Mining is part of a higher-level process known as Knowledge Discovery in Databases (KDD).<\/a><\/p>\n\n\n\n Research in the field of \u201cArtificial Intelligence\u201d (AI)<\/strong> attempts to replicate human perception and human action by machines. All technologies, such as machine learning, deep learning, or neural networks used in the context of the provision of intelligence, which have until recent times been the preserve of human beings, now fall under the umbrella of AI. While \u201cstrong\u201d AI describes a previously unattained state in which a machine is capable of anything, \u201cweak\u201d AI deals with transferring individual skills of human beings to machines, such as the recognition of text, image content, playing, speech recognition and so on. Rapid progress has been made in this area in recent years. \u201cMachine Learning\u201d, \u201cDeep Learning\u201d, \u201cNatural Language Processing\u201d (NLP) and \u201cneural networks\u201d are therefore only sub-areas of AI, or even partial sub-areas within these sub-areas. [3]<\/a><\/a><\/p>\n\n\n\n Machine Learning describes mathematical techniques that enable a system (machine) to independently generate knowledge from experience. ML algorithms<\/strong> are used to recognize patterns in existing data sets, to make predictions or to classify data. Mathematical models can be used to gain new insights based on these patterns. The applications range from music and film recommendations in private to the improvement of marketing campaigns, customer services or even logistics routes in the area of business.<\/p>\n\n\n\n Although \u201cMachine Learning\u201d uses linear regression, decision tree algorithms, or cluster analysis, as does data mining, one major difference is that it is less focused on recognizing new patterns in data than on developing appropriate models to discover known patterns in new data. [4]<\/a><\/p>\n\n\n\n Artificial Intelligence and Machine Learning (ML) are not even particularly new technologies, but they have only just begun to play a role in practical use relatively recently. The prerequisites for learning systems and corresponding algorithms have not existed for that long, or had simply been too expensive to meet in the past. But we now have sufficient computing capacities at our disposal and access to the very large amounts of data necessary to make such solutions viable. According to a survey, German companies are already quite advanced. In a recent survey, one in five companies in Germany said they were already actively using ML technology, while 64 percent said they were taking a close interest in the topic and as many as four out of five respondents said that ML would be one of the core technologies of the fully digitized company of the future. [5]<\/a><\/p>\n\n\n\n At the moment, machine learning, deep learning, and cognitive computing are just some of the more well-known notions among a constellation of AI terms that are not easy to define. For this purpose, the dimensions of application and degree of autonomy can be used. For the most part, ML systems<\/strong> have been developed and trained for applications. For example, they detect defective products during the manufacturing process within the framework of quality control. The task is clearly defined, there are no game rooms.<\/a><\/p>\n\n\n\n Deep Learning systems<\/strong>, on the other hand, are able to learn independently. In cooperation with large amounts of training data, neural networks learn to learn independently and to make decisions and can thus perform specific tasks \u2013 for example, the identification of cancer cells in medical images.<\/a><\/p>\n\n\n\n The third type of AI is Cognitive Computing<\/strong>. These systems are distinguished by the fact that they can take on tasks and make decisions by performing an assistance function or even acting as a substitute for human being, while at the same time dealing with ambiguity and uncertainty. Examples include case management in insurance, customer service hotlines, or diagnostics in hospitals. Even though a high degree of autonomy can already be achieved in such areas, there is still a long way to go on the path to true artificial intelligence with autonomous cognitive abilities. In the meantime, companies would be well-advised to take a close look at the feasible use cases, of which there are already many.[6]<\/a><\/p>\n\n\n\n Sources:<\/p>\n\n\n\n [1] http:\/\/wirtschaftslexikon.gabler.de\/Archiv\/2346\/multivariate-statistik-v9.html<\/a><\/p>\n\n\n\n [2] http:\/\/wirtschaftslexikon.gabler.de\/Archiv\/57691\/data-mining-v9.html<\/a><\/p>\n\n\n\n [3] http:\/\/t3n.de\/news\/ai-machine-learning-nlp-deep-learning-776907<\/a><\/p>\n\n\n\n [4] http:\/\/www.enzyklopaedie-der-wirtschaftsinformatik.de\/lexikon\/daten-wissen\/Business-Intelligence\/Analytische-Informationssysteme\u2013Methoden-der-\/Data-Mining\/index.html<\/a><\/p>\n\n\n\n [5] Cf. https:\/\/www.computerwoche.de\/a\/machine-learning-darum-geht-s,3330413<\/a><\/p>\n\n\n\nData Mining<\/h2>\n\n\n\n
Artificial Intelligence<\/h2>\n\n\n\n
Machine Learning<\/h2>\n\n\n\n
Deep Learning<\/h2>\n\n\n\n
Cognitive Computing<\/h2>\n\n\n\n