Approachable Data Mining Tutorials for the Non Data Miner

A list of several sources to learn data science in a hands-on format – The most approachable machine learning course available. And it’s free. – Provides data sources, forums, scenarios, and real-world competitions to teach data mining – Tutorial on Deep Learning – introduction to machine learning image analysis algorithms – Interactive introduction to R Language

Big Data Technology Strategy: Is Hadoop Already Outdated?


Is Hadoop Already Outdated?

Logical architecture of the Hadoop stack
The Hadoop Ecosystem

An article posted to Information Age 18 February, 2013 Teradata CTO Stephen Brobst highlights the schism that has overtaken traditional Decision Support and the new-age Big Data camp, noting at a recent Stanford University very-large-database conference “The Hadoop guys were saying, ‘relational databases are dead, SQL programming is for dinosaurs, long live the new kings Hadoop and MapReduce.'”  (Swabey, 2013 ).  The inclusion of the Hadoop platform by name and the technology’s rapid ascendancy is striking in its proliferation progressing from initial release to core services in multinational platforms in less than six years (Hadoop Releases, 2013), yet it represents the lion’s share of the commercial Big Data marketplace.  Fanatical zeal aside, should it be the sole platform for knowledge management and creation?

Much is made of the dimensions by which we assign special treatment to “Big Data”.   These facets are known popularly as “The Three V’s”, which are defined by Gartner as “high-volume, high-velocity and high-variety information assets”.  Additional V’s are sometimes added to suit the audience as necessary including Veracity (What is big data?, 2013), Variability, and Value (Fan, 2013).  In the December 2013 issue of the ACM SIGKDD, Wei Fan and Albert Bifet explore the current and future state of Big Data.  They allude to signals that the technology adoption has overshot the technical ecosystem’s ability to give it proper perspective providing seven factors they consider to be controversial (Fan, 2012):

  •  There is no need to distinguish Big Data analytics from data analytics, as data will continue growing, and it will never be small again […]
  •  Big Data may be a hype to sell Hadoop based computing systems. Hadoop is not always the best tool […]
  • In real time analytics, data may be changing. In that case, what it is important is not the size of the data, it is its recency […]
  • Claims to accuracy are misleading […]
  • Bigger data are not always better data.  It depends if the data is noisy or not, and if it is representative of what we are looking for […]
  • …[Is it] ethical that people can be analyzed without knowing it […]
  • Limited access to Big Data creates new digital divides […]

Further supporting Fan and Bifet’s arguments, Stephen Brobst notes, “A lot of people are talking about the ‘velocity of big data’ but if that just means that data values are updating quickly, it’s nothing new.  What’s new is the velocity of change in the structure of data.” (Swabey, 2013).

Google (noticeably silent in the Big Data marketplace) abandoned the batch processing approach underlying Hadoop in favor of a real-time, service-based processing architecture originally called Dremel and outlined in a paper from Google research (Melnik, 3010).  Google’s BigQuery cloud service, used extensively at Google internally, takes a differing tack that “builds on ideas from web search and parallel DBMSs”—core competencies for the company.  In a January 2013 consortium organized by IBM and Arizona State University, Dr. K. Selcuk Candan (Candan, 2013) highlights six key outcomes which may be summarized as a need for better data fusion, data analysis algorithms, data models, scalable architectures, and real-time analysis.  While several vendors are visibly out front with custom Hadoop builds for real-time analysis, two non-Hadoop projects, S4 in the Apache Incubator and the production-ready Storm ( show promise a general-purpose parallel computing engines.

While Apache Hadoop project has staged an impressive entrance, broken through the Relational and OLAP paradigms, and shown the viability of open source software, I intend to keep an eye on the companies that have avoided the hype such as Google (Regalado, 2013) and observe as the market polarizes into real-time analysis and those who never needed it.



Candan, K. Selcuk. (2013, June 25). Hunting for the Value Gaps in Data Management, Services, and Analytics.  Retrieved from .

Fan, Wei, and Albert Bifet, Mining Big Data: Current Status, and Forecast to the Future, December 2014, Vol. 4, Issue 2.  Downloaded from

Gilyadov, Camuel. (2013, July 2). OpenDremel: Google BigQuery / Dremel implementation.  Retrieved from

Hadoop Releases. (2013, June 14). Retrieved from

Melnik, Sergey, Andrey Gubarev, Jing Jing Long, Geoffrey Romer, Shiva Shivakumar, Matt Tolton, Theo Vassilakis (2010). “Dremel: Interactive Analysis of Web-Scale Datasets”. Proc. of the 36th International Conference on Very Large Data Bases (VLDB).

Regalado, Antonio. (2013, June 11).  Just Don’t Call it Big Data: Why Google fears the totalitarian connotations of the buzzword big data.  Retrieved from

Swabey, Pete. (2013, February 18).  Teradata seeks compromise in the big data Holy Wars.  Retrieved from

What is big data? (n.d.). Retrieved from


Dictionary of Data Mining Terms

The elements of big data analytics has roots in statistics, knowledge management, and computer science. Many of the data mining terms below appear in these disciplines but may have different connotation or specialized meaning when applied to our problems. The problems of massive parallel processing and the specialized algorithms employed to perform analysis in a distributed computing environment are enough to require specialized treatment.

Data Mining Terms


Accuracy A measure of a predictive model that reflects the proportionate number of times that the model is correct when applied to data
Bias Difference between expected value and actual value
Cardinality Data mining terms indicating the number of different values a categorical predictor or OLAP dimension can have. High cardinality predictors and dimensions have large numbers of different values (e.g. zip codes), low cardinality fields have few different values (e.g. eye color).
CART Classification and Regression Trees. A type of decision tree algorithm that automates the pruning process through cross validation and other techniques.
CHAID Chi-Square Automatic Interaction Detector. A decision tree that uses contingency tables and the chi-square test to create the tree. Classification. The process of learning to distinguish and discriminate between different input patterns using a supervised training algorithm. Classification is the process of determining that a record belongs to a group
Cluster Centroid most typical case in a cluster.  The centroid is a prototype. It does not necessarily describe any given case assigned to the cluster.
Clustering The technique of grouping records together based on their locality and connectivity within the n-dimensional space. This is an unsupervised learning technique.
Collinearity The property of two predictors showing significant correlation without a causal relationship between them
concentration of measure any set of positive probability can be expanded very slightly to contain most of the probability the average of bounded independent random variables is tightly concentrated around its expectation
Conditional Probability The probability of an event happening given that some event has already occurred. For example the chance of a person committing fraud is much greater given that the person had previously committed fraud
Confidence The likelihood of the predicted outcome, given that the rule has been satisfied.
convergence of random variables a sequence of essentially random or unpredictable events can sometimes be expected to settle down into a behaviour that is essentially unchanging when items far enough into the sequence are studied
correlation number that describes the degree of relationship between two variables
Coverage A number that represents either the number of times that a rule can be applied or the percentage of times that it can be applied
Cross-validation The process of holding aside some training data which is not used to build a predictive model and to later use that data to estimate the accuracy of the model on unseen data simulating the real world deployment of the model.
Data Mining Process Define the problem. Select the data. Prepare the data. Mine the data. Deploy the model. Take business action.
Discrete Fourier Transform Concentrates energy in first few coefficients
Entropy A measure often used in data mining algorithms that measures the disorder of a set of data
Error Rate A number that reflects the rate of errors made by a predictive model. It is one minus the accuracy
Expectation–maximization algorithm for estimating parameters where there exist significant missing or inferred values
Expectation-Maximization (EM) Solves estimation with incomplete data. Iteratively use estimates for missing data and continue until convergence
Expert System A data processing system comprising a knowledge base (rules), an inference (rules) engine, and a working memory
Exploratory Data Analysis The processes and techniques for general exploration of data for patterns in preparation for more directed analysis of the data
Factor Analysis A statistical technique which seeks to reduce the number of total predictors from a large number to only a few “factors” that have the majority of the impact on the predicted outcome.
Fuzzy Logic A system of logic based on the fuzzy set theory
Fuzzy Set A set of items whose degree of membership in the set may range from 0 to 1
Fuzzy System A set of rules using fuzzy linguistic variables described by fuzzy sets and processed using fuzzy logic operations
Genetic Algorithm Optimization techniques that use processes such as generic combination, mutation, and natural selection in a design based on the concepts of  revolution
Genetic Operator An operation on the population member strings in a genetic algorithm which are used to produce new strings
Gini Index A measure of the disorder reduction caused by the splitting of data in a decision tree algorithm. Gini and the entropy metric are the most popular ways of selected predictors in the CART decision tree algorithm
Hebbian Learning One of the simplest and oldest forms of training a neural network. It is loosely based on observations of the human brain. The neural net link weights are strengthened between any nodes that are active at the same time.
Hill Climbing A simple optimization technique that modifies a proposed solution by a small amount and then accepts it if it is better than the previous solution. The technique can be slow and suffers from being caught in local optima
Hypothesis Testing The statistical process of proposing a hypothesis to explain the existing data and then testing to see the likelihood of that hypothesis being the explanation
ID3 Decision Tree algorithm
Intelligent Agent A software application which assists a system or a user by automating a task. Intelligent agents must recognize events and use domain knowledge to take appropriate actions based on those events.
Itemset An itemset is any combination of two or more items in a transaction
Jackknife Estimate estimate of parameter is obtained by omitting one value from the set of observed values. Allows you to examine the impact of outliers.
Kernel a function that transforms the input data to a high-dimensional space where the problem is solved
k-Nearest Neighbor A data mining technique that performs prediction by finding the prediction value of records (near neighbors) similar to the record to be predicted
Kohonen Network A type of neural network where locality of the nodes learn as local neighborhoods and locality of the nodes is important in the training process. They are often used for clustering
Latent variable variables inferred from a model rather than observed
Lift A number representing the increase in responses from a targeted marketing application using a predictive model over the response rate achieved when no model is used
Machine Learning A field of science and technology concerned with building machines that learn. In general it differs from Artificial Intelligence in that learning is considered to be just one of a number of ways of creating an artificial intelligence
maximum likelihood method for estimating the parameters of a model
Maximum Likelihood Estimate (MLE) Obtain parameter estimates that maximize the probability that the sample data occurs for the specific model. Joint probability for observing the sample data by multiplying the individual probabilities.
Mean Absolute Error AVG(ABS(predicted_value – actual_value))
Mean Squared Error (MSE) expected value of the squared difference between the estimate and the actual value
Memory-Based Reasoning (MBR) A technique for classifying records in a database by comparing them with similar records that are already classified. A form of nearest neighbor classification.
Minimum Description Length (MDL) Principle The idea that the least complex predictive model (with acceptable accuracy) will be the one that best reflects the true underlying model and performs most accurately on new data.
Model A description that adequately explains and predicts relevant data but is generally much smaller than the data itself
Neural Network A computing model based on the architecture of the brain. A neural network consists of multiple simple processing units connected by adaptive weights
Nominal Categorical Predictor A predictor that is categorical (finite cardinality) but where the values of the predictor have no particular order. For example, red, green, blue as values for the predictor “eye color”.
Ordinal Categorical Predictor A categorical predictor (i.e. has finite number of values) where the values have order but do not convey meaningful intervals or distances between them. For example the values high, middle and low for the income predictor
Outlier Analysis A type of data analysis that seeks to determine and report on records in the database that are significantly different from expectations. The technique is used for data cleansing, spotting emerging trends and recognizing unusually good or bad performers
overfitting The effect in data analysis, data mining and biological learning of training too closely on limited available data and building models that do not generalize well to new unseen data. At the limit, overfitting is synonymous with rote memorization where no generalized model of future situations is built
Point Estimation estimate a population parameter. May be made by calculating the parameter for a sample. May be used to predict value for missing data.
Predictive model model created or used to perform prediction. In contrast to models created solely for pattern detection, exploration or general organization of the data
Predictor The column or field in a database that could be used to build a predictive model to predict the values in another field or column. Also called variable, independent variable, dimension, or feature.
Principle Component Analysis A data analysis technique that seeks to weight the importance of a variety of predictors so that they optimally discriminate between various possible predicted outcomes
Prior Probability The probability of an event occurring without dependence on (conditional to) some other event. In contrast to conditional probability
Purity/Homogeneity the degree to which the resulting child nodes are made up of cases with the same target value
Radial Basis Function Networks Neural networks that combine some of the advantages of neural networks with those of nearest neighbor techniques. In radial basis functions the hidden layer is made up of nodes that represent prototypes or clusters of records
Receiver Operating Characteristic (ROC) The area under the ROC curve (AUC) measures the discriminating ability of a binary classification model. The larger the AUC, the higher the likelihood that an actual positive case will be assigned a higher probability of being positive than an actual negative case. The AUC measure is especially useful for data sets with unbalanced target distribution (one target class dominates the other).
Regression A data analysis technique classically used in statistics for building predictive models for continuous prediction fields. The technique automatically determines a mathematical equation that minimizes some measure of the error between the prediction from the regression model and the actual data
Reinforcement Learning A training model where an intelligence engine (e.g. neural network) is presented with a sequence of input data followed by a reinforcement signal
Root Mean Squared Error SQRT(AVG((predicted_value – actual_value) * (predicted_value – actual_value)))
Sampling The process by which only a fraction of all available data is used to build a model or perform exploratory analysis. Sampling can provide relatively good models at much less computational expense than using the entire database
Segmentation The process or result of the process that creates mutually exclusive collections of records that share similar attributes either in unsupervised learning (such as clustering) or in supervised learning for a particular prediction field
Sensitivity Analysis The process which determines the sensitivity of a predictive model to small fluctuations in predictor value. Through this technique end users can gauge the effects of noise and environmental change on the accuracy of the model
Simulated Annealing An optimization algorithm loosely based on the physical process of annealing metals through controlled heating and cooling
Sparsity This means that a high proportion of the nested rows are not populated.
Statistical Independence The property of two events displaying no causality or relationship of any kind. This can be quantitatively defined as occurring when the product of the probabilities of each event is equal to the probability of the both events occurring
Stepwise Regression Automated Regressions to identify most predictive variables.  1st regression finds most predictive, 2nd regression finds most predictive given 1st regression.
Supervised Algorithm A class of data mining and machine learning applications and techniques where the system builds a model based on the prediction of a well defined prediction field. This is in contrast to unsupervised learning where there is no particular goal aside from pattern detection.
Support The relative frequency or number of times a rule produced by a rule induction system occurs within the database. The higher the support the better the chance of the rule capturing a statistically significant pattern.
Term Definition
Time-Series Prediction The process of using a data mining tool (e.g., neural networks) to learn to predict temporal sequences of patterns, so that, given a set of patterns, it can predict a future value
Unsupervised Algorithm A data analysis technique whereby a model is built without a well defined goal or prediction field. The systems are used for exploration and general data organization. Clustering is an example of an unsupervised learning system
Visualization Graphical display of data and models which helps the user in understanding the structure and meaning of the information contained in them


This overview of data mining terms is part of a publication, “Dictionary of Data Mining Terms” due out in publication in November 2013 by Don Krapohl.  This post does not use any content from, but acknowledges a similar work by Dr. Vincent Granville at, also containing a significant number of data mining terms.