Blog Data Mining techniques of Big Data

Data Mining techniques of Big Data

The Challenge

As the businesses, governments, and society tries to understand and organize how collective needs and behavior work themselves out in real life, Data has proven to be best for studying patterns to be able to predict needs and behavior with a certain degree of accuracy and predicting time or patterns to come in the future.

In the beginning, research was limited to mostly to asking direct questions and merely taking the word of the respondent for it, but as science developed and researcher started putting their longitudinal leanings into hypothesis, that encompass a whole population, for specific hypothesis, which were then proven or disproven to conclude.

One of the key things that data also helps analysts uncover is the connection, motivations behind behavior which could be causally connected to the action eventually, completing the chain from intention to action. What this adds is the heart of argument as to why something works for so many people, but try as they may, this was applicable and predictable for few constraints

One, primary research is expected to understand people based on their direct response or claimed behavior, however, when this behavior was decomposed, their researchers find several routes of needs and logic which the consumer took to achieve his goal or need to be met. Now to be able to understand the different patterns of behavior at the national or global scale, it is imperative for primary research to interview a huge sample, making such research unaffordable. So was never pursued, except for Government, which does censuses.

Data Mining

But all that changed with the advent of machines that can record data. What this does is, it documents the final action of the consumer, and as known, over a period, nearly most people will perform the most common duties and chores, albeit in their way. What gives recorded the best advantages are, in the order of importance:

1. A person’s action like a transaction at a store is more like an answer questioned for a market researcher, i.e., which product will you buy? So now instead of asking, 300 of 30,000 consumers who buy, with recorded data, I can get answers of 30,000 instead of 300, this is what makes it big data, but what is so crucial is even more mind-blowing for an analyst

2.In the primary research, a person is aware that he/she is being asked to answer a survey, human beings when conscious of their action or response, or specifically when they feel like they are being tested, provide filtered responses, which many times have not panned out by the time of predicted action, this could be for many reasons. One of the key ones that fascinated the marketing people was, why would the same person, who claimed to use brand A would go ahead and buy Brand B in the store, even after entering the store with the express intention of buying brand A, for that individual the reason was a better discount, which prompted a change in the consumer purchase. This is revealing and when analyzed was found that there was an amazing array of POS activities that can be done to turn tables on the competition at retail – national, or global scale. To be able to get ‘end action’ of the population, to be able to count observations of everyone, is what we know as big data.

3. Where big data comes into its own, is when software programs start writing programs. They can ask the system to observe behavior life and respond as a Chatbot, using AI, is just one of the learning’s of being able to use big data. The idea is that the data set is so big, that the findings from it can be sized and controlled for. This helps in everything for giving out free meals in disasters, creating new vaccines or predicting how many mothers will buy a brand of milk for their kids.

Big data, is amazing because it attempts to retrace the steps like a detective after the action has been committed, it is like a live investigation being carried out and this is where it brings the real insight and real application of the data come into its own.

Last but not the least, is to be able to bring together all kinds of data to create a complete picture of any particular situation, so it can combine, qualitative responses with quantitative responses to the sales data and map out the consumer journey without ever having to bother them. Practitioners know that Big data is cheaper, faster, and better to work with.

d So to formally put it…

Big data is the way researchers or analysts think of the data in technical terms to meet an objective or answer, this is done by systematically extracting information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

This includes never-ending types of statistical techniques, analysis, and hypothesis as more and more patterns are used to explain ever more sophisticated hypothesis which can pin down a behavior or action, as the analysis grows and the hypothesis perfects itself, eventually, theoretically, the analyst should be able to predict everyone’s behavior, with logic and the route they would take. Such predictability is game-changing for managers and politics. Big data was originally associated with three key concepts: volume, variety, and velocity.

Big data is a term that describes the large volume of data – both structured and unstructured – and also shown above, unobserved behavior is more authentic, which inundates a business on a day-to-day basis over a long period. However, it’s what organizations do with the data that matters.

Insights from any size of data, when analyzed almost always, will help improve or fine-tune a positive decision, a way forward, logically. It is also used by governments to predict the needs and future of peoples, cities, states. Big data reveals in increasing efficiency in the identified clusters of uniformity, making it possible for economists and engineers to produce at the leanest possible delivery mechanism.

Big data is used in marketing for explaining the underlying current of motivations and behaviors that emboss the customer insights in a way that leads to tunnel vision leading to simpler products.

And when online, and live, by looking at data as it happens or in the recent past, from social media, GPS-enabled devices, and CCTV footage and various other points of where consumers generate data. Another one of the popular ways is used in creating a strategy to keep consumers coming for more.

As the name suggests, a process of logic, guided by an objective, discerns or distributes the data set into different groups based on the thresholds defined. One of the most common examples of this is poor, middle, and rich class. The government can define, how much money does one needs to be which socio-economic class and thus what are their needs.

The same strategy is used by businesses to predict demand, plan resources, and respond with supply. The wheel that keeps the world rolling is better served by understanding the needs in greater detail and the solutions can be planned tailored for a particular class. So to say that same Unilever also has 500USD shampoo and also 50cent shampoo sachet, they can sell the same thing to everyone, by just being more relevant en masse, this is the power of classification analysis. And it is easy to do.

In this analysis, the data manager retrieves important and relevant information from the data and metadata. It is used to classify different data in different classes, as defined by the business.

Classification leads to clustering in the basic sense that either leads to segments, data records into different segments called classes. However, in classification, knowledge of different classes or clusters is available. So, in classification analysis, it is a case of simply applying the algorithms which then decide how to classify the data based on the defined parameters.

Another classic example of classification analysis would be our Outlook email. In Outlook, they use certain algorithms to characterize an email is legitimate or spam.
There are several other challenges that the developers have to work with. Here are some of the most common challenges:

ASSOCIATION RULE LEARNING: is a method used to identify atypical, uncommon but common relations (dependency modeling) between different variables in large databases. To put simply, despite several variables influencing an eventual outcome, we can observe and build causalities, which can explain large or almost universal variances in outcomes. Revealing thresholds, where outcomes vary and marking the points for further investigation. It has been proven to be so good, that, In IT, programmers use association rules to build programs capable of machine learning.

This technique is used to quantify and model hidden patterns in the data that can be used to identify variables within the data and the concurrence of different variables that appear very frequently in the dataset, indicating that, there has a causal relationship or at least a very strong correlation, the scalar and vector of its being measurable.

Association rules are used most for forecasting customer behavior. It is highly recommended in the retail industry analysis, as it has components that involve real-time changes in consumer’s decisions, and it is also being the place that has traditionally presented very workable business solutions.

For example, this technique is used to determine shopping basket data analysis, product clustering, and catalogue design and store layout.

ANOMALY OR OUTLIER DETECTION : Going back to the absolute basics of doing any analysis at all, lays the idea of variance in data, which is measurable in standard deviations from the average. As we all know the average is how close to most people are most likely to behave, a model’s robustness and its application in real life depend on this variance that a solution or product can address and resources required to produce it and price it can be sold at, all depends on average behavior and almost static range. However, this is not possible in real life, due to different circumstances; there are always people, consumers, who behavior is so far above or below the normal that it wouldn’t fit any patter and skew the average of the rest of the group, making the purpose of analysis useless. Therefore, to be able to predict a large cluster of people, people who fall outside those boundaries, either the first or second variation for the average, are then classified as outliers. Outlier detection and treatment refers to the observation for data items in a dataset that do not match an expected pattern or expected behaviour. Anomalies are also known as outliers, noise, deviations, or exceptions.

Often, they provide critical and actionable information. An anomaly is an item that deviates considerably from the common average within a dataset or a combination of data. These types of items are statistically aloof as compared to the rest of the data and hence, it indicates that something out of the ordinary has happened and requires additional attention.

This technique is used in a wide range of industry, marketing, and applications such as security, fraud, health monitoring, anomaly, or event detection in sensor networks, and detecting in general disturbances in an ecosystem.


As the name suggests, a cluster is a group of people, who are like each other in many ways but have individual minor differences. In big data, the cluster is a collection of data objects; those objects are similar within the same cluster.

This means that within the cluster, it allows for data to vary on an individual case, however, when seen as a group, they have bespoke differences which help mark the thresholds and define how the cluster vary from each other. Clustering analysis is the process of discovering groups and clusters in the data in such a way that the degree of association between two objects is highest if they belong to the same group and lowest otherwise.

In most marketing or management scenarios involved for planning, one needs to understand what the sizes of population are and how they vary and how to allocate resources for best for each of the different group for better conversion to sale or outcome. Clusters are often backed by different analysis like correlation and regression, which then goes on to decompose and define how the groups vary from each other. The thing about the cluster that makes them useful is that they can be defined more and more in terms of softer variables and not just hard economic variables.


At the heart of most of the analysis is to be able to explain why something happens, what are the specific influences or people who make them behave in different ways. But also what is more important is how these influences or variables need to be manipulated to change the outcomes of the choices. A price drop leads to an increase in sales, which is a simple example of regression analysis, which can also help predict, how much share can be gained by manipulating each of the variables. This gives marketing budgets defined variables so that then the managers can plan for execution and allocate their resources accordingly.

In statistical terms, a regression analysis is a process of identifying and analyzing the relationship among variables. It can help you understand the characteristic value of the dependent variable changes if any one of the independent variables is varied. This means one variable is dependent on another; however, the reverse may not be true, hence further cementing the logic to be followed.
Data Mining techniques of Big Data

That is why it is the most commonly used for forecasting and predicting behavior.

These were some of the most commonly used analysis on big data, there are countless other techniques and applications in the variety of these analyses. All of these techniques can help analyze different data from different perspectives.

Now you know to decide the best technique to summarize data into useful information – information that can be used to solve a variety of business problems to increase revenue, customer satisfaction, or decrease unwanted cost. The applications are endless. The idea is that the data is so big and so comprehensive that almost ever variance in behavior can be explained and accounted for and if needed manipulated for a better outcome.

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