Knowledgeminer Releases New Data Mining App For Mac
Make Better Decisions Based on Relevant Data. 'Lovely maths and algorithms. Nice and simple product. Feel that it can significantly assist me.' Conrad Mackenzie, Australia Insights (formerly KnowledgeMiner) is the next-generation, self-organizing, predictive modeling app for the Mac. Find unknown relevant relationships in your scientific, business or personal data stored in Excel, for example, by using outstanding, automated knowledge mining methods for prediction, classification, identification or simulation tasks in various fields.
Users in nearly any field without being an expert in modeling can analyze data sets and build powerful models, which help to gain new insights into complex phenomena, predict future behavior, simulate 'what if' questions, and identify methods of controlling processes. Insights is used for predicting sales, production and demand, engineering problems, climate change, health or life sciences-related questions, or mining collections of data from government agencies. Make your business more smart and be one step ahead your competitors by implementing predictive behavior into your business processes or services. Insights provides cutting-edge self-learning modeling technologies for short- to longer-term forecasting of complex processes such as market prices, market demand, or sales figures. Insights v3.1 now adds Autoupdating of models. Run simulations on historical data stored in Excel for backtesting, for example, or use it on real-time data for continuous forecasting of processes several steps ahead.
Knowledgeminer Releases New Data Mining App For Mac Mac
All results are reported and stored in Excel for further use. Insights comes with documentation, extra literature, sample data and models such as:.
Forecasting hourly bike sharing demand 48h ahead. Crude Oil price forecasting till 2025 and related questions. Indoor temperature forecasting for energy consumption optimization. Monthly forecast of Global Mean Temperature and Ozone concentration till 2017. Fetal Monitoring and evaluation or prediction of ecotoxic effects of chemicals Forecasting hourly bike sharing demand 48h ahead. Within seconds. Humans have for centuries been seeking proxies for real processes.
A substitute that can generate reliable information about a real system and its behaviour is called a model and they form the basis for any decision. It is worth building models to aid decision making, because models make it possible to:. Identify the relationships between cause and effect. This leads to a deeper understanding of the problem at hand by deriving an analytical relationship between them,. Predict the respective objects can expect over a finite future time span, but also to experiment with models. The ability to continuously make predictions from auto-associative past patterns is the core of human intelligence. Simulate the objects' behaviour by experiment with models, and thus answer 'what-if' questions essential to decision-making,. Control the objects by finding suitable means to effect the objects and enforce a specific behaviour.
There are many cases in practice where it is impossible to create analytical models using classical theoretical systems analysis or common statistical methods since there is incomplete knowledge of the processes involved. Environmental, medical and socio-economic systems are but three examples. In contrast, inductive models obtained by knowledge mining are derived from real physical data and represent the relationships implicit within the system without or with only little knowledge of the physical processes or mechanisms involved.
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