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--- dissertation [2016/08/28 23:00]
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-====== Introduction ======
+[[Diss_Introduction|Introduction]]
+[[Diss_RunningExample|Running Example]]
-Knowledge discovery in databases (KDD) and data mining offer
+[[ClusteringOverview|Clustering Overview]]
-enterprises and other large organisations a set of methods to
+[[distancefunctions|Distance Functions]]
-discover previously unknown relations among data---which are often
+[[partitioningclustering|Partitioning Clustering]]
-called patterns, e.g. [[diss_bibliography#han|han]], [[diss_bibliography#Frawley|Frawley]], [[diss_bibliography#ester|ester]]. With
-these patterns enterprises gain deeper insight into their context
-and are able to use them for better decision-making.
-Frawley, Piatesky-Shapiro and Matheus define //knowledge
-discovery// as follows:
-> Knowledge discovery is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data [p. 3][[diss_bibliography#Frawley|Frawley]].
-It is common practise
-to use the term //knowledge discovery in databases// or short
-//kdd// instead to stress the most commonly used use case. Yet, it is
-convenience to use the term //kdd// also for knowledge discovery in
-data that are not kept in databases but in texts or data streams.
-The term //data mining// denotes the step of the //kdd// process
-in which sophisticated methods analyse the data for patterns of
-interest \cite{han}\cite{ester}. The herein used techniques are
-further called //data mining techniques//. The techniques
-clustering, classification, and association rule mining are the
-most-commonly used data mining techniques.
-Quality of patterns found by data mining algorithms and the time
-needed to find them are the measures of interest in //kdd// projects.
-The quality of a found pattern determines its usability---for
-instance, to guide a decision. Additionally, when mining data in
-large databases, time is an issue, too.
-This dissertation focusses on the problem of pattern quality and
-time of //kdd// projects. Recently, many approaches have been
-introduced to improve specific //kdd// analyses. However, these
-approaches try to optimise a single analysis.
-In contrast to existing work, this dissertation presents a novel
-way to improve //kdd// projects in terms of shorter project time and
-higher quality of results by optimising a set of analyses instead
-of optimising each analysis individually.
-Therefore, the remainder of this section is organised as follows:
-To be able to show open issues of past work, it first introduces
-the //kdd// process as is. Later, this section shows where there are
-open issues of the //kdd// process. Last but not least, this section
-gives the basic idea of how to improve the //kdd// process in terms
-of runtime or quality of results.
-{{ wiki:kdd.png | KDD process}}
-===== A First Introduction into the kdd Process =====
-This subsection gives a short introduction into the //kdd// process.
-It gives the information that is necessary to show open issues
-which is topic of the next subsection. A more detailed discussion
-of the //kdd// process will follow in section \ref{kdd process
-discussion}.
-Figure \ref{kdd process} shows the //kdd// process with all its
-phases. The //kdd// process consists of the phases pre-processing,
-data mining, and evaluating resulting patterns. Using evaluated
-patterns for decision-making is not part of the //kdd// process but
-Figure \ref{kdd process} contains this item to denote that the
-//kdd// process does not exist per se but is embedded in a broader
-economic context.
-The first step in a KDD process is selecting data that might
-contain patterns one is interested in. Some data mining algorithms
-require data as an input that are consistent with a specific data
-schema. The //pre-processing// phase consists of all activities
-that intend to select or transform data according the needs of a
-data mining analysis.
-In the //data mining// phase a data mining expert---further
-referenced as analyst---analyses the data using sophisticated data
-mining algorithms. The outcome of these algorithms is a set of
-patterns that contain potential new knowledge.
-\begin{figure}
-\centering
-\includegraphics[width=12cm]{iteratingnecessary.eps}
-\caption{Issue A: Insufficient results inflict additional
-iterations} \label{iteratingnecessary}
-\end{figure}
-The resulting patterns must be evaluated before they can be used.
-This happens during the \emph{evaluating} phase in which an
-analyst tests whether the patterns are new, valid and interesting
-or not.
-If there are no patterns that fulfill all the above conditions,
-the KDD process or a part of it has to be repeated once more. The
-reason why a KDD process has to be iterated can be a bad choice of
-parameter values or a bad selection of data.
-===== Open Issues of the kdd Process =====
-Although there exist approaches that improve the runtime of
-instances of the //kdd// process, time is still an open issue of the
-//kdd// process---quality of results is the other one. Hence, we will
-discuss open issues concerning the runtime of instances of the
-//kdd// process first before discussing issues related with quality.
-Algorithms with long runtime and many iterations during an
-instance of the //kdd// process increase the runtime of this
-instance. If the data set which an algorithm is analysing is very
-large then even the runtime of algorithm of well-scaling
-algorithms is high. The consequence of a long runtime of an
-algorithm is that the analyst who started it is slowed down in
-ones work by the system until the algorithm terminates.
-Iterating steps in an instance of the//kdd// process is necessary
-because the best values of the used algorithm's parameters and the
-best set of data for a specific analysis are initially unknown.
-Assuming the opposite would mean that an analyst performs an
-analysis the result he/she already knows. Yet, that would conflict
-with the definition of //kdd// that patterns must be new.
-\begin{figure}
-\centering
-\includegraphics[width=12cm]{multipleinstances.eps}
-\caption{Issue B: Multiple isolated //kdd// instances use the same
-data} \label{multipleinstances}
-\end{figure}
-Consequentially, it is common practise to run data mining
-algorithms several times---each time with a different combination
-of parameter values and data. More precisely, the analyst chooses
-the data and values that will most likely bring the best results
-best to one's current knowledge. Even an unsuccessful attempt
-commonly gives the analyst a better insight into the relations
-among the analysed data. Future analyses will be more likely
-successful. Figure \ref{iteratingnecessary} depicts an instance of
-the //kdd// process that requires two iterations.
-When an analyst re-does an analysis with different settings, these
-analyses are not identical but several tasks are very similar.
-More specifically, both analysts share several intermediate
-results as later chapters of this dissertation will show.
-Obviously, processing the same intermediate results more than once
-means to waste time. Yet, previous work introduces techniques
-which are able to re-use results of a previous analysis when
-parameters change. If an analyst uses a different subset of the
-data he/she must compute all results of this new analysis from
-scratch.
-Two analyses that share the computation of some intermediate
-results is not limited to analyses of the same instance of the
-//kdd// process. Moreover, a set of analyses can also share several
-tasks or parts of them. Especially if these analyses use the same
-data set and also share the same type of analysis, several
-intermediate results can be identical. Figure
-\ref{multipleinstances} shows two instances of the //kdd// process
-that use the same data set to analyse. Although the goals of both
-instances are different, both instances need to cluster the same
-table. Yet, parameter values and used attributes differ. Later
-chapter will show that is possible to pre-compute common
-intermediate results for analyses when only attributes and
-parameters vary.
-Re-doing the same steps of analyses would be tolerable if the time
-needed to compute an analysis is negligible---which is not the
-case, as illustrated in the next paragraphs.
-//Pre-processing// and most //data mining methods// require
-accessing all data---which is very time-consuming in large
-databases exceeding 1GB in size. Some algorithms are inapplicable
-to large data sets without supplying proper database index
-structures. For instance, a single run of the DBSCAN \cite{DBSCAN}
-clustering algorithm scanning ten thousand tuples (less than 1 MB
-in size) endured approximately eleven hours on a SUN Sparc
-workstation \cite[pp. 149-151]{stoettinger}. The reason for this
-long runtime is that DBSCAN requires multiple scans of the
-selected data.
-Although some data mining algorithms like the clustering
-algorithms BIRCH \cite{zhang96birch} and CLARANS
-\cite{ng94efficient} are very efficient and scale better in the
-number of tuples, the minimum time needed for
-\emph{pre-processing} and \emph{data mining} is still too long for
-interactively analysing the data. During the tests for this
-dissertation the Oracle 9i database required few minutes to answer
-a simple \emph{SELECT COUNT(*) FROM table} query when the size of
-the table exceeded half a gigabyte---each time the query required
-accessing values of a specific attribute response time increased
-significantly to up to ten minutes.
-The databases of mail order companies or large warehouses exceed
-one gigabyte by far. For instance, think of a company operating
-supermarkets. When there are only $300$ small supermarkets in
-which there are only $300$ customers buying $5$ items per day and
-the database requires only $512$ byte to store a sold item, the
-data set that is daily stored requires more than $0.25$ GB disc
-space.
-A long runtime of an algorithm or a pre-processing task means that
-an analyst has to wait longer for a computer to finish its job
-than he/she can analyse the resulting patterns.
-Zhang et alii state that the most time-consuming part of
-data-mining algorithms is scanning the data while operations in
-main memory are negligible \cite{zhang96birch}. This statement is
-consistent with the test results of this dissertation. As a
-consequence, approaches of improving data mining algorithms must
-focus on minimising the number of required database scans.
-Avoiding scanning the database at least once is impossible. Even
-taking a sample requires at least one scan of the
-data---otherwise, the so-taken sample would not be representative.
-Some data mining algorithms already require only the minimum
-number of one database scan. BIRCH is a hierarchical clustering
-algorithm requiring only one scan, Bradley et alii have proposed
-in \cite{Bradley} an EM (expectation maximisation) clustering
-algorithm, or Kanungo et alii have presented in
-\cite{kanungo-efficient} a $k$-means-like algorithm, to name only
-few examples of clustering approaches that require only one scan
-of the data.
-If existing data mining algorithms are nearly as fast as the
-theoretical limit but even this minimal time is too long for
-interactive working, the source of major improvements in runtime
-must be elsewhere. The next subsection gives the basic idea of the
-approach of this dissertation that is capable to reduce the needed
-runtime below the current limit. The research question of this
-dissertation are a direct consequence of this approach.
-In many approaches which we will discuss later, one can decrease
-runtime on behalf of quality of the results of the //kdd// process.
-Hence, quality is always an additional issue when discussing
-improvements of runtime. Yet, improving quality and runtime do not
-exclude each other. Thus, the effect on quality is an additional
-research question of any approach changing the //kdd// process. Yet,
-the benefit of improving quality of results depends on the
-specific instance of the //kdd// process. For instance, if a company
-can improve the classification of customers who are about to
-cancel their contracts with the company then each increase in
-quality, i.e. the accuracy of classification, is beneficial.
-===== Basic Idea of Approach and Research Questions =====
-The //kdd// process as it is shown in figure \ref{kdd process}
-describes the sequence of steps of a single analysis. Especially,
-the //kdd// process does not take into account that analysts do
-several analyses---not necessarily all of them at the same time
-but many analyses within a year. The discussion of the last
-subsection indicated that further improvement of analyses is
-necessary to enable working continuously on data analyses.
-However, improving a single analysis is improper because the
-theoretical limit of one scan is not fast enough. Changing this
-isolated point of view can be a source of major improvement which
-will be shown later.
-\bigskip \emph{The core idea of this dissertation is to re-think the
-knowledge discovery in databases process. Instances of the KDD
-process should no longer be regarded as single independent process
-instances but should be regarded as dependent instances that can
-profit from each other.}
-//Further, if the specific setting of a future KDD process
-instance is unknown but several tasks of the instance are known,
-these known tasks should be done in anticipation.//
-\bigskip
-If two or more //kdd// process instances share the same
-time-consuming tasks or parts of them then it is reasonable to do
-those tasks only in the first occurring instance---it would be
-wasted time to re-do such a task in the second and future
-instances.
-\bigskip \emph{The underlying basic assumption is that there are
-process instances that have time-consuming tasks in
-common.}\bigskip
-The research questions this dissertation intends to answer are
-consequents of the above-mentioned basic idea and basic
-assumption. To be specific, the research questions of this
-dissertation are as follows:
-  - How do //kdd// analyses differ from each other?
-  - Are there intermediate results shared by multiple analyses?
-  - Can (intermediate) results of an analysis affect the quality of the result of another analysis?
-If the variability of analyses is known, it is possible to
-identify items that are common in all analyses of a specific type
-of analysis. Additionally, one can identify the range of some
-parameters. If there are only a few values for a specific
-parameter the analyst can choose of, one can pre-compute the
-results for all of these potential values. When the analyst
-finally chooses the actual value for an analysis, the result has
-already been computed by the system.
-If there are intermediate results of an analysis that other
-analyses might need than these other analyses can profit of these
-intermediate results. Hence, the answer to the second research
-question is needed to improve the //kdd// process: If common
-intermediate results are identified, one can pre-compute them in
-anticipation to save time in succeeding analyses.
-The third research question focusses on the aspect of quality of
-analyses. Taking several analyses into account can also affect the
-quality of the results of each analysis. For instance, knowledge
-gained in an analysis can help getting better results in another
-analysis by biasing the succeeding analysis.
-A specific type of analysis needs to perform tasks of a specific
-type. Some of these tasks depend on parameters of the analyst,
-others do not. It is possible to pre-compute the results of tasks
-which are independent of analyst's input.
-The data set which the system processes in a specific task is the
-only differing factor of the same independent tasks of analysis of
-the same type. As the number of tables in a database is limited,
-it is possible to pre-compute intermediate results of commonly
-used types of analyses of all large tables.
-However, there still remain tasks depending on parameters which
-cannot be pre-computed. Thus, it is necessary to split analyses
-into parts that can be processed anticipatory and tasks that
-cannot, as illustrated in figure \ref{somekddinstances}. A single
-instance of a supporting process pre-computes intermediate results
-for two instances of an altered //kdd// process that consists only of
-parameter-specific tasks. Later sections will introduce the
-supported process as parameter-specific //kdd// process and the
-supporting process as parameter-independent //kdd// process.
-The instance of the parameter-independent //kdd// process
-pre-computes intermediate results and keeps them up-to-date when a
-table which has been used to pre-process has changed, i.e. there
-are new, removed, or altered tuples in the table.
-The instances of the parameter-specific //kdd// process use the
-pre-computed intermediate results either to process the final
-result for higher performance or to bias the computation to
-receive better results. Improving speed with pre-computed
-intermediate results corresponds with the second research
-question, while improving quality by biasing corresponds with the
-third research question.
-The above-mentioned concept of anticipatory pre-computing ideally
-support analysing data warehouses containing few but very large
-fact tables. As the combination of data warehouses and //kdd// is a
-common combination in companies having mass data, this concept
-offers much potential for many companies.
-Some approaches of related work already arise from the same idea
-of pre-processing but are limited to complete tasks---most of them
-are tasks of the pre-processing phase. This dissertation
-introduces an approach that splits tasks into smaller junks and is
-able to increase the number of pre-computable tasks that way.
-Additionally, this approach extends the idea of pre-computing to
-the data mining phase. Hence, it is more broadly applicable than
-existing work.
-\begin{figure}
-\centering
-\includegraphics[width=12cm]{somekddinstances.eps}
-\caption{Improving the //kdd// process by using pre-computed
-intermediate results and auxiliary data instead of tuples}
-\label{somekddinstances}
-\end{figure}
-This dissertation will present a technique to increase the
-performance of instance-specific pre-processing tasks to make them
-so fast that analysts can work on analyses without being slowed
-down by the data mining system.
-In addition to the \emph{pre-processing} phase, the \emph{data
-mining} phase also has got high potential for improvement.
-This dissertation presents techniques to enhance the performance
-of data mining techniques and intends to improve the //kdd// process
-in terms of quality and speed that way. For being more specific,
-it is useful to distinguish the different data mining techniques
-association rule mining, classification, clustering, and
-regression at this point.
-Association rule mining requires finding frequently occurring sets
-of items before finding association rules among them. If the
-frequent item sets that are valid in the set of all transactions
-are once determined it is possible to determine the frequent item
-sets that are valid in subsets of all transactions. This has been
-sufficiently shown in other work. This work will discuss those
-works for reasons of completeness but will contribute only a minor
-new idea.
-Classification algorithms use a set of data to train a function
-that is called a classifier which is able to predict the class of
-a tuple using the tuples' attribute values. The set of data that
-is used for training typically contains only few
-tuples---consequentially, scaling algorithms is not a problem. The
-quality of the resulting classifier is the most concerning issue
-of classification. This work will discuss quality-improving
-classification approaches and contribute some new ideas how to
-improve the quality of a classifier by using auxiliary data gained
-as by-products of other //kdd// process instances.
-Clustering algorithms produce a set of clusters. All clustering
-algorithms but density-based clustering algorithms represent
-clusters with a set few describing attributes which varies from
-algorithm to algorithm. For instance, \EM clustering algorithms
-generate a probabilistic model consisting of $k$ random variables
-and store mean and deviation of each variable, while $k$-means and
-related algorithms represent cluster by their euclidian mean. It
-is easy to compute mean and standard deviation of a cluster, when
-the number of the cluster's tuples, the linear sum of its tuples
-and their sum of squares is known, which will be demonstrated
-later.
-If the tuples within a subset of the data are very similar to each
-other, they will be part of the same cluster in most times. Thus,
-it is possible to build a subtotal of all of these tuples.
-Determining the mean by summing up all tuples or summing up
-subtotals will produce the same result as long as there are no
-tuples included that are part of a different cluster. If there are
-such tuples then the result might but need not be erroneous. The
-experimental results will show that this error is tolerable.
-This dissertation will discuss approaches improving clustering by
-aggregating tuples to sub-clusters in detail and will present new
-ideas. Especially, the re-use of sub-clusters will be enhanced in
-comparison to existing approaches like \cite{zhang96birch} or
-\cite{bradley98scaling} such that it is possible to find
-sub-clusters in anticipation.
-Additionally, this dissertation will also discuss other popular
-approaches improving the performance of analyses such as sampling.
-A running example shall illustrate the concepts of this
-dissertation. Therefore, the next section introduces a running
-example which is used to demonstrate more complex parts of this
-dissertation's concepts.

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