The need to analyze information from streams arises in a variety of
applications. One of its fundamental research directions is to mine sequential
patterns over data streams. Current studies mine series of items based on the
presence of the pattern in transactions but pay no attention to the series of
itemsets and their multiple occurrences. The pattern over a window of itemsets
stream and their multiple occurrences, however, provides additional capability
to recognize the essential characteristics of the patterns and the
inter-relationships among them that are unidentifiable by the existing
presence-based studies. In this paper, we study such a new sequential pattern
mining problem and propose a corresponding sequential miner with novel
strategies to prune the search space efficiently. Experiments on both real and
synthetic data show the utility of our approach.

本文提出一种新的序列模式挖掘问题，建议使用新的策略剪枝搜索空间，以识别模式的基本特征和之间的相互关系，并通过在真实和合成数据上的实验证明了该方法的有用性。

考虑多次出现的频繁串事件增量挖掘

Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences

Knowledge discovery from data is an inherently iterative process. That is,
what we know about the data greatly determines our expectations, and therefore,
what results we would find interesting and/or surprising. Given new knowledge
about the data, our expectations will change. Hence, in order to avoid
redundant results, knowledge discovery algorithms ideally should follow such an
iterative updating procedure.
With this in mind, we introduce a well-founded approach for succinctly
summarizing data with the most informative itemsets; using a probabilistic
maximum entropy model, we iteratively find the itemset that provides us the
most novel information--that is, for which the frequency in the data surprises
us the most---and in turn we update our model accordingly. As we use the
Maximum Entropy principle to obtain unbiased probabilistic models, and only
include those itemsets that are most informative with regard to the current
model, the summaries we construct are guaranteed to be both descriptive and
non-redundant.
The algorithm that we present, called MTV, can either discover the top-$k$
most informative itemsets, or we can employ either the Bayesian Information
Criterion (BIC) or the Minimum Description Length (MDL) principle to
automatically identify the set of itemsets that together summarize the data
well. In other words, our method will `tell you what you need to know' about
the data. Importantly, it is a one-phase algorithm: rather than picking
itemsets from a user-provided candidate set, itemsets and their supports are
mined on-the-fly. To further its applicability, we provide an efficient method
to compute the maximum entropy distribution using Quick Inclusion-Exclusion.
Experiments on our method, using synthetic, benchmark, and real data, show
that the discovered summaries are succinct, and correctly identify the key
patterns in the data.

本文提出了一种基于最大熵模型的算法 MTV，该算法采用迭代更新的方式发现给定数据中最丰富的项目集合，从而构建出简洁但非冗余的数据摘要，并通过实验证明其有效性。

用最具信息量的项集简洁地总结数据

Summarizing Data Succinctly with the Most Informative Itemsets

Mining itemsets that are the most interesting under a statistical model of
the underlying data is a commonly used and well-studied technique for
exploratory data analysis, with the most recent interestingness models
exhibiting state of the art performance. Continuing this highly promising line
of work, we propose the first, to the best of our knowledge, generative model
over itemsets, in the form of a Bayesian network, and an associated novel
measure of interestingness. Our model is able to efficiently infer interesting
itemsets directly from the transaction database using structural EM, in which
the E-step employs the greedy approximation to weighted set cover. Our approach
is theoretically simple, straightforward to implement, trivially parallelizable
and retrieves itemsets whose quality is comparable to, if not better than,
existing state of the art algorithms as we demonstrate on several real-world
datasets.

通过 Bayesian 网络的生成模型和新颖的趣味度量，我们的方法能够从交易数据库中直接高效地推断出最有趣的项目集，并且在多个真实世界的数据集上取得与现有先进算法相当甚至更好的效果。