The collaboration of scientists and specialists in the field are needed for the following reasons<\/span>:<\/p>\n1) Only the specialists of each field involved can decipher what sectors are most important in the discovery phase, while also knowing the dimensions and variables.<\/p>\n
2) Only scientists at the appropriate level are able to understand which theoretical background to use when modelling and solving the data problem.<\/p>\n
Once these points are addressed, it is possible to realise and deliver an application that will help managers work efficiently.<\/p>\n
To realise this work there are some corner stones<\/p>\n
1) Discovery: clustering, topological data analysis \u2026<\/p>\n
2) Relations between variables and groups of variables: regression, correlation.<\/p>\n
3) What is the relative weight of each group and each variable?<\/p>\n
There are lots of methods that help to solve these problems all have their pros and cons. One of them is CFAR-m.<\/p>\n
CFAR-m<\/strong><\/p>\nIn summary, CFAR-m helps to describe complex reality with many interacting fields; to deliver metrics, make simulations, and build powerful models. Extracting more objective information from datasets helps to provide a better analysis and understanding and forms the foundations to building advanced solutions to problems, issues or situations.<\/em><\/p>\nIt can be applied in known fields or used to investigate clusters coming from patterns discovery tools<\/em><\/p>\nMain features of CFAR-m<\/strong><\/p>\n1) Automatic extraction of weightings; only data driven<\/p>\n
2) No reduction of variables (it accounts for all the variables for processing the calculus and delivering the results)<\/p>\n
3) Each item has its own vector of weight<\/p>\n
4) It shows the contribution of each variable (or group of variables) to the ranking (sensitivity)<\/p>\n
5) By taking into consideration all the variables without exception, CFAR-m is able to determine the level of influence (lots, some, and none) of each one. Variables are used to build simplified models that work in real-time. That said, in an ever-changing world, one needs to be able to detect and anticipate any changes. This is why CFAR-m is reused periodically to check that no major changes have occurred and that the influence of any previously non-influential variables has not grown exponentially in the interim. If that is the case, then those variables are integrated into the simplified model.<\/p>\n
6) Whilst CFAR-m can be used for aggregation and ranking purposes, CFAR-m is better as a tool to build advanced and sophisticated applications.<\/p>\n
CFAR-m can model complex relationships without needing any a priori assumptions about the distribution of variables (a major constraint of conventional statistical techniques).<\/p>\n
___________________<\/strong><\/p>\nDifferent stages:<\/strong><\/p>\n1) What do we want to measure (risk, governance etc.). This confirms which theoretical framework is relevant. Even though CFAR-m is a powerful tool, it must be correctly used. If you are not able to accurately describe what you want, specialists may potentially be required or even R&D conducted.<\/p>\n
2) What are the \u201cdimensions\u201d to take into account to get the results you want? If you don’t know, specialists may be required or even R&D conducted.<\/p>\n
3) What are the variables that represent each dimension? If you don’t know, specialists may be required or even R&D conducted.<\/p>\n
4) Information to provide in order to use CFAR-m: Sense of contribution of each variable to the ranking. That means that for each variable we have to clarify whether a higher value of a variable will push the ranking of the item towards the first ranking position (positive contribution) or have the opposite effect. CFAR-m delivers ranking and if we want to rank we must be able to clarify the contribution of each variable. If we do not know, further investigations will be needed or R&D must be conducted.<\/p>\n
5) Output: What results do you want to obtain? Ranking, contribution of variables, index, partial index, weightings, or\/and simulations?<\/p>\n
Conclusion:<\/strong><\/p>\nCFAR-m can be a precious data driven solution to understand Big Data and what happens inside each cluster at the level of each variable and dimension; get the contribution to the ranking (sensitivity) of each variable.<\/p>\n
Qualitative and quantitative aspects are the two faces of a same coin. The qualitative aspect of some techniques combined to the quantitative aspect of CFAR-m allows delivering a unique and powerful solution to the big data that deals with clusters while CFAR-m operates inside each cluster.<\/p>\n
CFAR-m can be combined with technologies to take in account the following aspects of big data that are also very important:<\/p>\n
–\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 complexity<\/strong> as indicator of disruption as a certain level complexity introduce instability;<\/p>\n–\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 uncertainty<\/strong><\/p>\nAs CFAR-m can aggregate many different dimensions without any presupposition it can be considered as a quite holistic tool.<\/p>\n","protected":false},"excerpt":{"rendered":"
Remi Mollicone is the Chairman of CFAR-m. CFAR-m is an original method of aggregation based on neural networks which can summarize with objectivity, the information contained in a large number of complex variables.\u00a0CFAR-m solves the major problem of fixing the subjective importance of each variable in the aggregation (It avoids the adoption of an equal […]<\/p>\n","protected":false},"author":20,"featured_media":4428,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jnews-multi-image_gallery":[],"jnews_single_post":[],"jnews_primary_category":[],"jnews_social_meta":[],"jnews_override_counter":[],"footnotes":""},"categories":[338,340],"tags":[389,396,973],"coauthors":[],"class_list":["post-4381","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-intelligence-and-analytics","category-data-science","tag-big-data-2","tag-big-data-applications","tag-cfar-m"],"_links":{"self":[{"href":"https:\/\/dataconomy.com\/wp-json\/wp\/v2\/posts\/4381","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dataconomy.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dataconomy.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dataconomy.com\/wp-json\/wp\/v2\/users\/20"}],"replies":[{"embeddable":true,"href":"https:\/\/dataconomy.com\/wp-json\/wp\/v2\/comments?post=4381"}],"version-history":[{"count":0,"href":"https:\/\/dataconomy.com\/wp-json\/wp\/v2\/posts\/4381\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dataconomy.com\/wp-json\/wp\/v2\/media\/4428"}],"wp:attachment":[{"href":"https:\/\/dataconomy.com\/wp-json\/wp\/v2\/media?parent=4381"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dataconomy.com\/wp-json\/wp\/v2\/categories?post=4381"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dataconomy.com\/wp-json\/wp\/v2\/tags?post=4381"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/dataconomy.com\/wp-json\/wp\/v2\/coauthors?post=4381"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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Remi Mollicone is the Chairman of CFAR-m. CFAR-m is an original method of aggregation based on neural networks which can summarize with objectivity, the information contained in a large number of complex variables.\u00a0CFAR-m solves the major problem of fixing the subjective importance of each variable in the aggregation (It avoids the adoption of an equal weighting or a weighting based on exogenous criteria).<\/p>\n