Automation on Key Perfomance Indicator

Future trends in data mining
Hans-Peter Kriegel · Karsten M. Borgwardt · Peer Kröger · Alexey Pryakhin · Matthias
Schubert · Arthur Zimek

Ludwig-Maximilians-Universität,
Oettingenstr. 67, Munich 80538, Germany
e-mail: kriegel@dbs.ifi.lmu.de

Over recent years data mining has been establishing itself as one
of the major disciplines in computer science with growing industrial impact.
Undoubtedly, research in data mining will continue and even increase over
coming decades. In this article, we sketch our vision of the future of data mining.
Starting from the classic definition of “datamining”, we elaborate on topics
that —in our opinion — will set trends in data mining.

1 The classic definition
With the advent of high-throughput experimental technologies and of highspeed
internet connections, generation and transmission of large volumes of
data has been automated over the last decade. As a result, science, industry,
and even individuals have to face the challenge of dealing with large datasets
which are too big for manual analysis. While these large “mountains” of data
are easily produced nowadays, it remains difficult to automatically “mine” for
valuable information within them.
“Data Mining”, often also referred to as “Knowledge Discovery in Databases”
(KDD), is a young sub-discipline of computer science aiming at the
automatic interpretation of large datasets. The classic definition of knowledge

discovery by Fayyad et al. from 1996 describes KDD as “the non-trivial process
of identifying valid, novel, potentially useful, and ultimately understandable
patterns in data” (Fayyad et al. 1996). Additionally, they define data mining as
“a step in the KDD process consisting of applying data analysis and discovery
algorithms[. . .]”.1 Over the last decade, a wealth of research articles on new
data mining techniques has been published, and the field keeps on growing,
both in industry and in academia.
In this article, we want to present our vision of future trends in data mining
and knowledge discovery. Interestingly, the four main topics we anticipate are
indirectly described in the classic definition of KDD.
Fayyad et al. defineKDDas searching for “patterns in data”. Originally, these
data were exclusively feature vectors, and they were static, i.e. without temporal
evolution. Over recent years, structured data such as strings and graphs and
temporal data such as data streams and time series have moved into the focus
of data mining, yet a lot remains to be done. We sketch our vision of future
developments in the field of mining complex objects in Sect. 2 and of temporal
data mining in Sect. 3.
In order to be able to “identify[ing] valid, novel [. . .] patterns in data”, a step
of preprocessing of the data is almost always required. This preprocessing has
a significant impact on the runtime and on the results of the subsequent data
mining algorithm. We discuss potential future progress in the understanding
and improvement of data preprocessing in Sect. 4.
Finally, knowledge discovery should detect “potentially useful, and ultimately
understandable patterns”. While important steps towards finding patterns
have been taken, non-experts may still encounter lots of difficulties, both
in applying data mining algorithms and interpreting their results. Advances in
making data mining algorithms more convenient in use and their results easier
to understand will have a positive impact on the datamining community. Sect. 5
presents some considerations in this respect.
2 Mining complex objects of arbitrary type
Modern automated methods for measurement, collection, and analysis of data
in all fields of science, industry, and economy are providing more and more
data with drastically increasing complexity of its structure. This growing complexity
is justified on the one hand by the need for a richer and more precise
description of real-world objects, and on the other hand by the rapid progress in
measurement and analysis techniques allowing versatile exploration of objects.
In order to manage the huge volume of such complex data, database systems
are employed. Thus, databases provide and manage manifold information concerning
all kinds of real-world objects, ranging from customers and molecules
to shares and patients.

Traditionally, relational databases keep this information in the form of attributes
from a certain range of possible domains, usually as numbers, dates, or
strings, or, possibly, restricted to a certain list of values. Object-relational databases
even allow one to define types to model arbitrary objects. In view of the
fact that the manual analysis of enormous volumes of complex data collected in
a database is infeasible in practice, there is an ever growing need for datamining
techniques that are able to discover novel, interesting knowledge in this complex
and voluminous data. Various methods for a wide range of complex data types
have been proposed over recent years, such as mining Multi-Instance Objects
(Dietterich et al. 1997; Gärtner et al. 2002; Weidmann et al. 2003; Kriegel et
al. 2006), or mining Multi-Represented Objects in a supervised (Kittler et al.
1998; Kriegel et al. 2004, 2005) or unsupervised manner (Yarowsky 1995; Blum
and Mitchell 1998; Kailing et al. 2004), or such as graph mining (Washio and
Motoda 2003).
However, data mining approaches usually tackle certain subtypes of data.
For example, item set mining is specialized to string data or lists of possible
values, many classification or clustering approaches need numerical data,
whereas others allowmining of categorical data. Often the different approaches
are combined to yield more appropriate results. For example, certain classes
of string kernels assess frequent substrings of sequences or texts and basically
count their occurrence (which resembles item set mining of some sort) to finally
build numerical features (comprising the number of occurrences of a certain
set of substrings). This in turn allows the application of methods engineered for
numerical data.
Modeling the world obviously creates a merely simplified representation.
Considering the real complexity of the objects as adequately as possible remains
a worthwhile goal for all directions of science. In computer science,
the concept of “object-oriented modeling” intends to describe complex objects
in a simple and thoroughly formalized manner. Here, attributes of an
object may be primitive types or objects themselves. Object-oriented and also
object-relational databases are able to present collections of such objects. It
seems highly desirable to be able to directly mine on these objects instead
of mining only parts of them (like their numerical attributes or numerical
models of their complex attributes). In recent years, many steps were taken
to mine objects modeled as graphs, or multi-represented, multi-relational or
multi-instance data. In some respects, these approaches are generalizations of
former approaches on unstructured data. On the other hand, the very same
approaches could be understood as adjustments to certain more general, but
not universal types of representations. We envision data mining being universalized
to tackle truly general objects. However, all these methods consider
static properties of objects. The picture of “object-oriented modeling” does
also include a modeling of behavior of objects, called “methods”, i.e. dynamic
properties. Furthermore, sequence diagrams or activity diagrams model the
chronology of behavior patterns.We consider these temporal aspects below (cf.
Sect. 3). Indeed, the behavior of software is a common data mining task (cf. e.g.

(Liu et al. 2005, 2006b)). Some steps towards directly mining object-oriented
systems can be found e.g. in Kanellopoulos et al. (2006).
Representing complex objects bymeans of simple objects like numerical feature
vectors could be understood as a way to incorporate domain knowledge
into the datamining process. The domain expert seeks ways to use the important
features of an object to e.g. classify new objects of the same type, eventually by
employing sophisticated functions to transform attributes of some type to features
of some other type. In the progress to generalized data mining one should
not disregard of course the advances made so far. Incorporating domain knowledge
fundamentally facilitates meaningful data mining. However, the specific
way tomake use of the domain knowledge of experts should also be generalized
to keep pace with more complex ways of mining complex objects.
Furthermore, the knowledge specific to a certain domain is increasing in
amount and complexity itself. Thus, it usually cannot be surveyed by a single
human expert anymore — the communities therefore in turn begin to provide
their knowledge often in databases or knowledge bases. Thus, in the future,
data mining algorithms should be able to automatically take reliable domain
knowledge available in databases into account in order to improve their effectiveness.
In order to process complex objects, distributed datamining seems to become
increasingly important (Liu et al. 2006a). Several application domains consider
the same complex object according to the same characteristics at different locations
and/or at different times (e.g. a patient can consult different doctors, or
continuous observation of a star is only possible by involving several telescopes
around the world). On the other hand, datamining on complex objects requires
significantly more computational power than data mining on feature vectors.
Finally, not all participants in a joint activity of data mining would like to share
all of their collected data, possibly in order to protect the privacy of their customers.
Thus, there is a growing need for distributed, privacy preserving data
mining algorithms for complex data.
3 Temporal aspects: dynamics and relationships
As indicated above, knowledge about the behavior of objects is an integral part
of understanding complex relationships in real-world systems and applications.
More and more modern methods for observation and data generation provide
suitable data to capture such complex relationships. Nonetheless,—sometimes
due to historical reasons—many research directions in data mining focus only
on static descriptions of objects or are not designed to take data with dynamic
behavior and/or relationships into account. Obviously, this is a severe limitation
since several important aspects of objects that are urgently needed to get a
better understanding of complex relationships are thus not considered for data
mining.
In addition to more complex data models, relationships between objects are
also determined by temporal aspects hidden in the data. Among others, there

are two major challenges derived from these temporal aspects which future
data mining approaches should be able to cope with.
First, data can describe developments over time or temporal mechanisms.
Typical examples are data streams and time-series data. Although mining these
temporal data types has received increasing attention in the past years, (see e.g.
Gaber et al. 2005; Keogh and Kasetty 2002 for overviews) there are still many
challenges to be addressed. For example, future data mining algorithms will
have to cope with finding different types of correlations in high-dimensional
time series or should explore novel types of similarity models for temporal
data in order to address different practical problems. Thus, following a more
application-oriented approach can offer novel challenges to the data mining
community.
Secondly, the patterns that are observed have also a temporal aspect, i.e. these
patterns usually evolve over time in dynamic scenarios. An important challenge
is to keep the patterns up-to-datewithout a complete recalculation from scratch.
In general, this is especially needed for mining data streams. However, also in a
dynamic database environment (which is usually the realistic scenario in most
companies) where inputs, deletions, and updates occur frequently, keeping patterns
up-to-date is a challenging problem of great practical importance (Achtert
et al. 2005; Domeniconi and Gunopulos 2001). In addition, it is very interesting
for many applications to monitor the evolution of patterns and to derive
knowledge concerning these changes or even the complete dynamic behavior
of patterns. Finding “patterns of evolving patterns” is an important challenge
which has not attracted a lot of research yet. Thus, data mining approaches for
these temporal aspects are envisioned, since they will play a key role in the
process of understanding complex relationships and behavior of objects and
systems.
4 Fast, transparent and structured data preprocessing
Anyone who has performed data mining on a real-world dataset agrees that
knowledge discovery is more than pure pattern recognition: Data miners do
not simply analyze data, they have to bring the data in a format and state that
allows for this analysis. It has been estimated that the actual mining of data
only makes up 10% of the time required for the complete knowledge discovery
process (Pyle 1999). In our opinion, the precedent time-consuming step of preprocessing
is of essential importance for data mining (Han and Kamber 2001).
It is more than a tedious necessity: The techniques used in the preprocessing
step can deeply influence the results of the following step, the actual application
of a data mining algorithm.We therefore feel that the role of the impact on and
the link of data preprocessing to data mining will gain steadily more interest
over the coming years.
A very nice example to support this claim originates from the field of data
mining in microarray gene expression data. As microarrays often miss to produce
data for a considerable amount of genes, one has to impute these missing

values for the following step of data analysis. Depending on which algorithm is
chosen for missing value estimation, the data mining results vary significantly,
as repeatedly reported in the microarray community (Troyanskaya et al. 2001;
Bø 2004; Jörnsten et al. 2005). This impact of data preprocessing on datamining
results is similarly reported in completely different application domains such as
operations research (Cronea et al. 2005).
In addition to format and completeness of the data, data mining algorithms
generally implicitly require data to originate from one single source. Entries
of different databases, however, may have different scales and may have been
generated by different experimental techniques with varying degree of noise.
Before data analysis starts, these differences between data from different sources
have to be balanced via data integration. Otherwise one risks discovering patterns
within the data that are caused by their different origins, and not by
phenomena in the application domain one wants to study. In addition to this
statistical integration, different formats and different semantics in disparate data
sources require further efforts in format and semantic integration, which form
long-standing challenges for the database community (Halevy 2003). Hence,
data integration is another central step in data preprocessing for knowledge
discovery.
It is important to point out that data preprocessing faces problems similar to
those of data mining. High-dimensional data can lead to scalability problems
for preprocessing algorithms, and missing value imputation and data integration
on structured data such as strings and graphs are even theoretically challenging
problems. Especially the statistical data mining community will be challenged
to design statistical tests and algorithms for efficient and scalable data preprocessing
on high-dimensional and structured data.
What will the future of data preprocessing for data mining look like? We
envision that preprocessing will become more powerful, faster and more transparent
than it is today. For fast and user-friendly data mining applications, data
preprocessing will be automated, and all steps undertaken will be reported to
the user or even interactively controlled by the user. A common data representation
and a common description language for data preprocessing will make it
easier for both computer and data miner to study and to decide which preprocessing
steps have been applied or should be applied to these data. Advanced
systems will automatically perform preprocessing in several different fashions
and report the results — and the differences between results of different preprocessing
techniques — to the user. Novel statistical tests and preprocessing
algorithms will enable the efficient preprocessing of large-scale, high-dimensional
and structured data. From a theoretical point of view, general classes
of preprocessing algorithms will likely be defined, such that the multitude of
existing techniques can be regarded as special cases of these broader categories.
In any case, there will be a lot to gain and a lot to study in preprocessing for
data mining over the next years.

5 Increasing usability
An ultimate trend can be subsumed under the slogan “increased usability”. Sections
2 and 3 have highlighted a growing demand for algorithms and systems
that can cope with more and more complex data objects which are structured
and observed over a certain period of time. Though future algorithms might
handle this complexity, the need for user guidance during preprocessing and
data mining will dramatically increase. Even in current data mining algorithms,
many established methods employ quite a few different input parameters. For
example, weighted Euclidian distance is very popular to tune distance-based
data mining algorithms, and edit distance (Bille 2005) is rather popular for
comparing trees, sequences and graphs. Though these methods often prove to
be very useful, it is still necessary to find out a good parameter setting before
exploiting the gained flexibility. In a wider sense, selecting a data mining algorithm
and a data transformation method itself can also be considered as a
problem of user guidance. The extensive use of parameters leaves data miners
with a large choice of algorithms and allows them to squeeze out that little bit
of extra performance by spending additional time on parameter tuning. However,
the gained flexibility comes at a price. Selecting the best possible methods
and finding a reasonable parameter setting are often very time consuming.
For future data mining solutions, this problem will become more dramatic
because more complex objects usually mean more parameters. Furthermore,
many approaches will employ several steps of data mining and each of them
will have its own parameters. For example, set-valued objects can be compared
by multiple kernels and distance measures (Eiter and Mannila 1997; Ramon
and Bruynooghe 2001; Gärtner et al. 2002) which compare the elements of each
set by using another kernel or distance measure in the feature space of single
instances. Therefore, data mining algorithms having a very small number of
parameters will gain more and more importance in order to reduce the necessary
user interaction. Other related aspects of usability are the intuitiveness
when adjusting the parameters and the parameter sensitivity. If the results are
not strongly dependent on slight variations of the parametrization, adjusting
the algorithms becomes less complex. To fulfill these requirements, we will first
of all distinguish two types of parameters and afterwards propose four goals for
future data mining methods.
The first type of parameter, called type I, is tuning datamining algorithms for
deriving useful patterns. For example, k for a k-NN classifier influences directly
the achieved classification accuracy and thus, the quality of classification.
The second type of parameter, called type II, is more or less describing the
semantics of the given objects. For example, the cost matrix used by edit distance
(Bille 2005) has to be based on domain knowledge and thus, varies from
application to application. The important aspect of this type of parameter is that
the parameters are used to model additional constraints from the real world.
Based on these considerations, the following proposals for future datamining
solutions can be formulated:

1. Avoid type I parameters if possible when designing algorithms.
2. If type I parameters are necessary, try to find the optimal parameter settings
automatically. For many data mining algorithms, it might be possible to
integrate the given parameters into the underlying optimization problem.
3. Instead of finding patterns for one possible value of a type II parameter, try
to simultaneously derive patterns for each parameter setting and store them
for postprocessing. Having all patterns for all possible parameter settings,
will allow one to gain important information. For example, if we want to
check if a certain pattern can be observed in a given data set and we do not
know which parameter setting to use, we could derive patterns using all possible
parameter values. Afterwards, we can check whether the pattern was
observed for one of the settings at all. It might be impossible to distinguish
meaningful from meaningless parameter settings, but for the same problem,
it might be possible to judge the quality of the results.
4. Develop user friendly methods to integrate domain knowledge where it is
necessary. Often the only applicable approach for selecting type II parameters
is to include additional domain knowledge into the data mining task.
However, transforming expert knowledge of a domain expert into a parameter
value is often quite difficult since the domain expert might not know the
meaning of the parameter.
In summary, future data mining applications will be capable to tune themselves
as far as possible, help domain experts to integrate their knowledge into
data transformation and generate a variety of possible patterns.
The second important aspect of increasing usability deals with the derived
patterns themselves. Currently, most of the data mining algorithms generate
patterns that can be defined in a mathematical sense. However, methods for
explaining the meaning of the found patterns are still in a minority. In the light
of the increased object complexity, this problem will gain additional importance.
Though it might be possible to interpret the meaning of a surface in a
given vector space, the patterns derived for more complex objects might not
be interpreted that easily even by an expert. Thus, it is likely that not only the
input data for data mining is getting more complex, but also the gained patterns
will increase in complexity. This trend is amplified by another challenge
that recently came up in the data mining community. In many applications, the
very general patterns derived by the standard methods do not yield a satisfying
solution to the given task. In order to solve a problem, the found patterns need
to fulfill a certain set of constraints which make them more interesting for the
application. Examples for this type of patterns are correlation clusters (Böhm
et al. 2004) and constrained association rules (Srikant et al. 1997). For more
complex data with mutual relationships, the derived patterns will be even more
complex. Thus, we can formulate additional challenges:
1. The patterns described by the datamining algorithms are still too abstract for
being understood. However, a pattern that is misinterpreted is of great danger.
For example, many data mining algorithms do not distinguish
between causality and co-occurrence. Consider an application that aims at

finding the reason for a certain type of disease. There is a great difference between
finding the origin of the disease or finding just an additional symptom.
Therefore, a very old challenge will remain very important for the data mining
community: Developing systems which derive understandable patterns
and making already derived patterns understandable.
2. As stated above, current algorithmsmostly focus on a limited set of standard
patterns. However, deriving these patterns often does not yield a direct and
complete solution tomany problems where datamining could be very useful.
Furthermore, with an increasing complexity of the analyzed data, it is likely
that the derived patterns will increase in complexity as well. Thus, a future
trend in data mining will be to find richer patterns.
3. A final task when working with future patterns is the increased number
of valid patterns, we might find in a large data set of complex objects, e.g.
through trying out several parameter values at once as mentioned above.
Therefore, the number of potentially valid patterns will be too large to be
handled by a human user, without a system organizing the results. Thus,
future systems must provide a platform for pattern exploration where users
can browse for knowledge they might consider as interesting.
To conclude, future datamining should generate a large variety of well understandable
patterns. Due to variations in the parameterizations, the number of
possibly meaningful and useful patterns will dramatically increase and thus, an
important aspect is managing and visualizing these patterns.
6 Conclusions
In this article, we surveyed major challenges for data mining in the years ahead.
We started with the classic definition of knowledge discovery and data mining.
Although we believe that this definition still describes the essence of this
important area of computer science, its interpretation has broadened over the
last couple of years and will continue to do so in the future. We highlighted
what is our vision of future interpretations of this definition.
First, we started with the type of “patterns in data” which knowledge discovery
is examining. While original data mining concentrated on vectorial data,
future data will predominantly be stored in much more complex data types and
data mining will have to cope with this increasing volume of structured data.
Another aspect of “patterns in data” in the future is the increasing importance
of studying their evolution over time. Considering time, allows to observe the
dynamics of patterns as well as the behavior and the interactions of data objects.
Second, the data to be studied is usually drawn from several sources. For
this reason, another important trend in data mining will be the growing importance
of data preprocessing and integration, ensuring that the “patterns in data”
found are “valid” on the complete set of data objects and not just on a particular
subset.
Third, an ultimate trend that data mining faces is increased usability to
detect “understandable patterns”, and to make data mining methods more

user-friendly. If future data mining methods have to handle all this complex
input and intelligent preprocessing, it is very likely that the user will have to
adjust more and more switches and knobs before getting any result. Hence,
achieving user-friendliness with transparent or even reduced parameterization
is a major goal. Usability is also enhanced by finding new types of patterns that
are easy to interpret, even if the input data is very complex.
Although no human being can foretell the future, we believe that there are
plenty of interesting new challenges ahead of us, and quite a few of them cannot
be foreseen at the current point of time.
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