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  • #16
    Data Mining Software

    Dear All,I didn't know where to put this question; but I guess it belongs here. What are the demands for data mining software patents like? Ex Siri, language processing, medical imaging classification, ... Do these companies use these algorithms off the shelf or do they patent all of it? I know the market is hot for data scientists/machine learning engineers now, but is patent law also a viable path for them?
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    • #17
      Data mining is a process that involves human intelligence, any software can only help with data analytics at most.
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      • #18
        Data Mining Software

        Dear All,I didn't know where to put this question; but I guess it belongs here. What are the demands for data mining software patents like? Ex Siri, language processing, medical imaging classification, ... Do these companies use these algorithms off the shelf or do they patent all of it? I know the market is hot for data scientists/machine learning engineers now, but is patent law also a viable path for them?
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        • #19
          Data mining is an interdisciplinary subfield of computer science.[1][2][3] It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.[1] The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.[1] Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[4]

          The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.[5] It also is a buzzword[6] and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java[7] (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons.[8] Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.

          The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.

          The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
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          • #20
            nice topic
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            • #21
              No i do Know Data MIning Software
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              • #22
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                • #23
                  With every interaction, your customers reveal valuable insights about themselves. But it can be easy to miss these insights when they’re scattered throughout systems or departments.
                  IBM® SPSS® Modeler is a powerful, versatile data mining workbench that helps business users consolidate data, spot hidden patterns or trends, and use this knowledge to make predictions and decisions

                  • Create powerful predictive models quickly and intuitively, without programming.
                  • Accelerate the entire data mining process for faster ROI.
                  • Analyze all data sources for maximum insight.
                  • Integrate findings enterprise-wide for better business results.
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                  • #24
                    Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.

                    The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Jav (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.
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                    • #25
                      Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
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                      • #26
                        Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
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                        • #27
                          Originally posted by Ashton01 View Post
                          Data mining is a process that involves human intelligence, any software can only help with data analytics at most.
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                          • #28
                            Watson from IBM. Watson Analytics is a smart data analysis and visualization service you can use to quickly discover patterns and meaning in your data – all on your own. With guided data discovery, automated predictive analytics and cognitive capabilities such as natural language dialogue, you can interact with data conversationally to get answers you understand. Whether you need to quickly spot a trend or you have a team that needs to visualize report data in a dashboard, Watson Analytics has you covered.
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