5,759 research outputs found
An interactive approach for the post-processing in a KDD process
Association rule mining is a technique widely used in the field of data mining, which consists in discovering relationships and/or correlations between the attributes of a database. However, the method brings known problems among which the fact that a large number of association rules may be extracted, not all of them being relevant or interesting for the domain expert. In that context, we propose a practical, interactive and helpful guided approach to visualize, evaluate and compare the extracted rules following a step by step methodology, taking into account the interaction between the industrial domain expert and the data mining expert
On the role of pre and post-processing in environmental data mining
The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed
Learning and Transferring IDs Representation in E-commerce
Many machine intelligence techniques are developed in E-commerce and one of
the most essential components is the representation of IDs, including user ID,
item ID, product ID, store ID, brand ID, category ID etc. The classical
encoding based methods (like one-hot encoding) are inefficient in that it
suffers sparsity problems due to its high dimension, and it cannot reflect the
relationships among IDs, either homogeneous or heterogeneous ones. In this
paper, we propose an embedding based framework to learn and transfer the
representation of IDs. As the implicit feedbacks of users, a tremendous amount
of item ID sequences can be easily collected from the interactive sessions. By
jointly using these informative sequences and the structural connections among
IDs, all types of IDs can be embedded into one low-dimensional semantic space.
Subsequently, the learned representations are utilized and transferred in four
scenarios: (i) measuring the similarity between items, (ii) transferring from
seen items to unseen items, (iii) transferring across different domains, (iv)
transferring across different tasks. We deploy and evaluate the proposed
approach in Hema App and the results validate its effectiveness.Comment: KDD'18, 9 page
CASP-DM: Context Aware Standard Process for Data Mining
We propose an extension of the Cross Industry Standard Process for Data
Mining (CRISPDM) which addresses specific challenges of machine learning and
data mining for context and model reuse handling. This new general
context-aware process model is mapped with CRISP-DM reference model proposing
some new or enhanced outputs
Ringo: Interactive Graph Analytics on Big-Memory Machines
We present Ringo, a system for analysis of large graphs. Graphs provide a way
to represent and analyze systems of interacting objects (people, proteins,
webpages) with edges between the objects denoting interactions (friendships,
physical interactions, links). Mining graphs provides valuable insights about
individual objects as well as the relationships among them.
In building Ringo, we take advantage of the fact that machines with large
memory and many cores are widely available and also relatively affordable. This
allows us to build an easy-to-use interactive high-performance graph analytics
system. Graphs also need to be built from input data, which often resides in
the form of relational tables. Thus, Ringo provides rich functionality for
manipulating raw input data tables into various kinds of graphs. Furthermore,
Ringo also provides over 200 graph analytics functions that can then be applied
to constructed graphs.
We show that a single big-memory machine provides a very attractive platform
for performing analytics on all but the largest graphs as it offers excellent
performance and ease of use as compared to alternative approaches. With Ringo,
we also demonstrate how to integrate graph analytics with an iterative process
of trial-and-error data exploration and rapid experimentation, common in data
mining workloads.Comment: 6 pages, 2 figure
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