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Data Mining Tools

Data Mining Tools and Techniques – How It Works
And Who Is Using It

Data mining is the process of discovering previously unknown, non-trivial, practically useful and accessible interpretations of information and intelligence in raw data that is necessary for making decisions in various areas of activity. Information found in the process of applying Data Mining methods should be non-trivial and previously unknown. The information should describe new relationships between properties and predict the values of some attributes based on others. The information uncovered should be applicable to new data with some degree of certainty, as usefulness lies in the fact that this information can bring certain benefits in its application.

What is Data Mining?

The process of extracting useful bits of information from streams of raw data for deriving conclusions using a variety of techniques and instruments is called Data Mining.

The process starts with business understanding that establishes the aim of Data Mining in itself. The process starts with identifying the main objectives and the end result that is being sought. Calculation and consideration of various variables, such as assumptions, resource constraints and other factors should be taken into account when establishing the ultimate goal of the process. The ideal goal of the process should be detailed and must take into account the interests of both the data miner and the clients in the case of business scenarios due to the interconnected nature of their interests.

The next stage involves the collection of the data resources in the form of strata, or blocks. The data could be compiled from databases and should be sanitized, or cleared of errors or potentially misleading deviations of data. It is difficult to ensure the sanity of the data, but errors can be reduced by cross-checking the available data with the questions set in the business goal. Once the data is verified, its quality should be ascertained and any missing data should be retrieved from additional sources.

The compiled data is first sorted and prepared for being analyzed in the ensuring steps of the process. The first step is to sort the data into an intelligible and understandable format that can be used for extracting information. The «noise» is removed along with inconsistencies from the data and missing pieces are filled in to constitute a wholesome database. An example of inconsistencies can be missing names and addresses of some clients in databases, which should be filled in to allow them to be used as useful parts of the data.

Next is the data transformation stage, which involves a series of operations needed to even out the level of the obtained data to make it understandable. The first step is the reduction of noise to level the quality of the data followed by the aggregation stage, which entails the compilation of data into homogeneous segments. Generalization follows to make sure low level and high level data are evened out through the use of hierarchical structures which lead to normalization that scales data either up or down. The final stage is attribution, which involves the construction of attributes that ultimately results in modeling. The modeling stage involves the application of mathematics to determine patterns.

The modeling techniques used should be based on the business objectives and the set used. Once the mathematical models are selected, testing should be conducted and produce results. The results are then ready to be presented as derivations that can be used to make decisions. The evaluation stage involves the comparison of the results with the business objectives. It is not excluded that the evaluation stage may produce additional objectives and lead to the requirement of more mining. With the results evaluated, a business decision can be made based on the information and the set is ready to be employed in operations. The entire process is riddled with numerous challenges, not the least of which is the need for highly qualified experts capable of properly interpreting data. Among the other challenges are the lack or excessive size of available data, as well as the poor quality of available data. The overly homogeneous or heterogeneous nature of the data may also be a challenge to its proper analysis or the derivation of results.

Benefits of Data Mining

The benefits are numerous and are all directly related to the implementation of various business objectives in the strategic sense.

Among the benefits is that it helps companies obtain crucial information on the processes taking place in their business environment on a micro and macro level. Vital business decisions can only be made on the basis of reliable information that can be extracted using Data Mining. The mining process is a cost-effective means of analyzing statistical data and helps identify potentially profitable trends and patterns. More importantly, the results derived can be used to improve existing business processes and analyze large volumes of data to identify weaknesses or strengths among both competitors and internal sectors of a company.

Though highly beneficial, Data Mining also has some disadvantages. An example is the malignant or nefarious use of mined information, such as the personal data of users being sold on the open market. Another challenge is the difficulty of using Data Mining software and the high cost of employing highly qualified specialists.The mathematical models applied can be erroneous and thus result in wrong derivations from the mined results. In addition, many techniques are inaccurate and can have dire consequences if the derived data is erroneous and is applied for making business decisions.

Data Mining Techniques

There are numerous methods for conducting Data Mining operations. The most popular and widely used are the following:

Classification is the process of retrieving important information for the purpose of classifying data into various segments for further analysis.

Clustering is another method for identifying similar info types to identify similarities and differences that can be later used to make derivations about the data.

Regression is a method for identifying relationships between different data clusters and variables for subsequent derivation of possible likelihoods of some factors in light of other circumstances.

Association is another technique that helps find associations between data types and allows to discover patterns in the data.

Outer detection is a technique that helps reveal certain data items that do not fit into patterns or are considered to be outliers or aberrant in their behavior, contradictory to what is expected of them. Outlier analysis is the common variation of the given technique that allows to identify the reasons for the appearance of extremes.

Sequential patterning allows discovering patterns in the sets that are similar and helps identify trends within certain timeframes.

Prediction is another important technique used in Data Mining that uses a number of other techniques in unison to identify trends. The key to proper application of the given technique is the correct application of techniques in analyzing past events for predicting their possible recurrence in the future.

Statistical and cybernetic methods for analyzing data in Data Mining are also being applied in the modern business environment. The former are based on accumulated knowledge and data, while the latter rely on different mathematical approaches.

There are a variety of statistical methods that can be applied in the process and include multivariate statistical analysis, relationships and time series analysis, and others. The cybernetic methods of Data Mining combine approaches based on mathematics and the use of artificial intelligence. The following are some of methods used in modern applications:

Clustering, which is the search and combination of similar structures and objects. This approach does not help to draw conclusions, but only finds and combines objects with common properties.

K-averages are an algorithm that helps determine hypotheses regarding the number of clusters, where the value of k may depend on previous studies, assumptions, or even intuition.

Bayesian networks are graphic structures representing probabilistic relations between a huge array of variables and serve to create probabilistic inference based on these variables.

Artificial neural networks have been a very popular topic, but before using a neural network, an analyst must first train the system to make sure it uses the necessary approaches and has sufficient data to apply.

There are a variety of statistical methods that can be applied in the process and include multivariate statistical analysis, relationships and time series analysis, and others. The cybernetic methods of Data Mining combine approaches based on mathematics and the use of artificial intelligence. The following are some of methods used in modern applications:

Clustering, which is the search and combination of similar structures and objects. This approach does not help to draw conclusions, but only finds and combines objects with common properties.

K-averages are an algorithm that helps determine hypotheses regarding the number of clusters, where the value of k may depend on previous studies, assumptions, or even intuition.

Bayesian networks are graphic structures representing probabilistic relations between a huge array of variables and serve to create probabilistic inference based on these variables.

Artificial neural networks have been a very popular topic, but before using a neural network, an analyst must first train the system to make sure it uses the necessary approaches and has sufficient data to apply.

Among the other important Data Mining techniques applied in modern business operations are the following:

Decision trees as part of machine learning based on large sets of branched data that allows to understand the effects on inputs and outputs.

Statistical techniques of various types tailored to business objectives, some of which may involve the use of neural networks and artificial intelligence constructs.

Data visualizations based on sensory perceptions and images, mane of which can be dynamic in nature. The identification of patterns in such data allows to highlight trends.

List of Important Data Mining Techniques Used
by Social Links

Social Links uses a number of specialized techniques in Data Mining in social networks. Among the most important techniques used are the following:

  • Statistical Techniques
  • Clustering Technique
  • Visualization
  • Induction Decision Tree Technique
  • Neural Network
  • Association Rule Technique

Social Media Data Mining

The process of social media Data Mining involves the collection and analysis of publicly available personal information on users. The data can include any personal information, such as names, locations, hobbies and others that users have freely shared.

The data is unstructured once compiled and needs to be analyzed and segmented to be of any use. Once the data is assembled, the aforementioned mining techniques can be applied based on the object of the search.

The results are then visualized for better interpretation and creating a clearer image of the overall data layout. Derivations can then be made and conclusions reached on the basis of the obtained data. The result of the search can reveal connections between individuals or organizations, hidden networking, conflicts of interest and much more.

Uses of Social Media Data Mining

The applications of Data Mining in social media are many and can include a wide variety of scenarios. Among the most common uses of Data Mining in social media are identification of associations among individuals and companies.

Another important use of Data Mining in social media is the identification of possible trends. Given the vast amount of dynamic data in social networks, companies can see emerging topics of interest to users and target audiences that can be developed into products and services.

Event detection is another application of Data Mining, as it involves the analysis of social network users to identify emerging or planned events. Such approaches are crucial for law enforcement and security authorities for identifying potential threats. The same can applied for identifying events that have recently taken place to take necessary measures for their prevention or remediation.

Though less frequently mentioned, another use of Data Mining is spamming that is applied by services and products companies to reach potential audiences. By relying on Data Mining, companies identify the interests of users and send them spam messages with offers.

In fact, Data Mining is used in a variety of industries ranging from social science to healthcare and education. One of the main applications of Data Mining is assortativity, which involves the identification of similarities between social network users to compile them into homogenous strata for later use as databases in making product and service offers.

Sentiment analysis is another important application of Data Mining for industries, as it helps gauge the level of attachment or positive or emotions of users towards companies. Such information is vital for reputation management and damage control in crisis situations in the information field based on the reactions of users to certain topics.

In addition, Data Mining can reveal important touch points for greater influence on audiences, for instance the identification of influencers and opinion leaders. Such tools become important in identifying good candidates for some positions in the marketing industry.

Homophily is also highly sought after as a tendency of people to become friends – a crucial factor for dating platforms and companies seeking to use recommendations as the basis of their advertising strategies.
Conclusion
Social media marketing is impossible without proper analysis and Data Mining is one of the most actively developing areas in IT. The following chart from FinancesOnline clearly demonstrates the benefits of such marketing strategies, which provide up to 93% greater exposure, 87% more traffic, 74% more leads and an increase in overall sales of up to 72%. Data mining and its proper implementation is the cornerstone of the success of such strategies.
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